healthcare-nlp/01.0.Clinical_Named_Entity_Recognition_Model.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"I08sFJYCxR0Z"},"source":["![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)"]},{"cell_type":"markdown","metadata":{"id":"FwJ-P56kq6FU"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/healthcare-nlp/01.0.Clinical_Named_Entity_Recognition_Model.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"nWu_IMfY4L1N"},"source":["# Clinical Named Entity Recognition (NER)"]},{"cell_type":"markdown","metadata":{"id":"Niy3mZAjoayg"},"source":["##  NER Model Implementation in Spark NLP\n","\n","  The deep neural network architecture for NER model in Spark NLP is BiLSTM-CNN-Char framework. a slightly modified version of the architecture proposed by Jason PC Chiu and Eric Nichols ([Named Entity Recognition with Bidirectional LSTM-CNNs](https://arxiv.org/abs/1511.08308)). It is a neural network architecture that automatically detects word and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering steps.\n","\n","  In the original framework, the CNN extracts a fixed length feature vector from character-level features. For each word, these vectors are concatenated and fed to the BLSTM network and then to the output layers. They employed a stacked bi-directional recurrent neural network with long short-term memory units to transform word features into named entity tag scores. The extracted features of each word are fed into a forward LSTM network and a backward LSTM network. The output of each network at each time step is decoded by a linear layer and a log-softmax layer into log-probabilities for each tag category. These two vectors are then simply added together to produce the final output. In the architecture of the proposed framework in the original paper, 50-dimensional pretrained word embeddings is used for word features, 25-dimension character embeddings is used for char features, and capitalization features (allCaps, upperInitial, lowercase, mixedCaps, noinfo) are used for case features."]},{"cell_type":"markdown","metadata":{"id":"huGcLHSP6oVb"},"source":["## Blogposts and videos:\n","\n","- [How to Setup Spark NLP for HEALTHCARE on UBUNTU - Video](https://www.youtube.com/watch?v=yKnF-_oz0GE)\n","\n","- [Named Entity Recognition (NER) with BERT in Spark NLP](https://towardsdatascience.com/named-entity-recognition-ner-with-bert-in-spark-nlp-874df20d1d77)\n","\n","- [State of the art Clinical Named Entity Recognition in Spark NLP - Youtube](https://www.youtube.com/watch?v=YM-e4eOiQ34)\n","\n","- [Named Entity Recognition for Healthcare with SparkNLP NerDL and NerCRF](https://medium.com/spark-nlp/named-entity-recognition-for-healthcare-with-sparknlp-nerdl-and-nercrf-a7751b6ad571)\n","\n","- [Named Entity Recognition for Clinical Text](https://medium.com/atlas-research/ner-for-clinical-text-7c73caddd180)"]},{"cell_type":"markdown","metadata":{"id":"okhT7AcXxben"},"source":["## Colab Setup"]},{"cell_type":"code","source":["# Install the johnsnowlabs library to access Spark-OCR and Spark-NLP for Healthcare, Finance, and Legal.\n","! pip install -q johnsnowlabs"],"metadata":{"id":"QnEWoyREND8D"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import files\n","print('Please Upload your John Snow Labs License using the button below')\n","license_keys = files.upload()"],"metadata":{"id":"uJ9NsgmSNHTy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from johnsnowlabs import nlp, medical\n","\n","# After uploading your license run this to install all licensed Python Wheels and pre-download Jars the Spark Session JVM\n","nlp.settings.enforce_versions=True\n","nlp.install(refresh_install=True)"],"metadata":{"id":"FfxtAw1kNKox"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from johnsnowlabs import nlp, medical\n","import pandas as pd\n","\n","# Automatically load license data and start a session with all jars user has access to\n","spark = nlp.start()"],"metadata":{"id":"kEq2r009NZXW","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712934899359,"user_tz":-180,"elapsed":7853,"user":{"displayName":"Monster C","userId":"08787989274818793476"}},"outputId":"04db2979-49b2-48fe-9afb-b386359967fa"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["👌 Detected license file /content/5.3.1.spark_nlp_for_healthcare.json\n","👌 Launched \u001b[92mcpu optimized\u001b[39m session with with: 🚀Spark-NLP==5.3.1, 💊Spark-Healthcare==5.3.1, running on ⚡ PySpark==3.4.0\n"]}]},{"cell_type":"code","execution_count":5,"metadata":{"id":"ELqzaf32MT6E","colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"status":"ok","timestamp":1712934899992,"user_tz":-180,"elapsed":638,"user":{"displayName":"Monster C","userId":"08787989274818793476"}},"outputId":"51ca3f36-e009-477b-c055-4d58a0fe6ce1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<pyspark.sql.session.SparkSession at 0x79d292aed9f0>"],"text/html":["\n","            <div>\n","                <p><b>SparkSession - in-memory</b></p>\n","                \n","        <div>\n","            <p><b>SparkContext</b></p>\n","\n","            <p><a href=\"http://bba1c37f35a2:4040\">Spark UI</a></p>\n","\n","            <dl>\n","              <dt>Version</dt>\n","                <dd><code>v3.4.0</code></dd>\n","              <dt>Master</dt>\n","                <dd><code>local[*]</code></dd>\n","              <dt>AppName</dt>\n","                <dd><code>John-Snow-Labs-Spark-Session 🚀 with Jars for: 🚀Spark-NLP==5.3.1, 💊Spark-Healthcare==5.3.1, running on ⚡ PySpark==3.4.0</code></dd>\n","            </dl>\n","        </div>\n","        \n","            </div>\n","        "]},"metadata":{},"execution_count":5}],"source":["spark"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"GiigSdavGaeg","executionInfo":{"status":"ok","timestamp":1712934899992,"user_tz":-180,"elapsed":4,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["from pyspark.sql import DataFrame\n","import pyspark.sql.functions as F\n","import pyspark.sql.types as T\n","import pyspark.sql as SQL\n","from pyspark import keyword_only"]},{"cell_type":"markdown","metadata":{"id":"zutgrNB3uuZT"},"source":["\n","\n","\n","\n","## Clinical NER Models\n","|index|model|index|model|index|model|index|model|\n","|-----:|:-----|-----:|:-----|-----:|:-----|-----:|:-----|\n","| 1| [jsl_ner_wip_clinical](https://nlp.johnsnowlabs.com/2021/03/31/jsl_ner_wip_clinical_en.html)  | 2| [jsl_ner_wip_greedy_clinical](https://nlp.johnsnowlabs.com/2021/03/31/jsl_ner_wip_greedy_clinical_en.html)  | 3| [jsl_ner_wip_modifier_clinical](https://nlp.johnsnowlabs.com/2021/04/01/jsl_ner_wip_modifier_clinical_en.html)  | 4| [jsl_rd_ner_wip_greedy_clinical](https://nlp.johnsnowlabs.com/2021/04/01/jsl_rd_ner_wip_greedy_clinical_en.html)  |\n","| 5| [ner_abbreviation_clinical](https://nlp.johnsnowlabs.com/2021/12/30/ner_abbreviation_clinical_en.html)  | 6| [ner_abbreviation_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/05/12/ner_abbreviation_emb_clinical_large_en.html)  | 7| [ner_abbreviation_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/05/12/ner_abbreviation_emb_clinical_medium_en.html)  | 8| [ner_ade_clinical](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_clinical_en.html)  |\n","| 9| [ner_ade_clinical_langtest](https://nlp.johnsnowlabs.com/2023/07/31/ner_ade_clinical_langtest_en.html)  | 10| [ner_ade_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/05/21/ner_ade_emb_clinical_large_en.html)  | 11| [ner_ade_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/05/21/ner_ade_emb_clinical_medium_en.html)  | 12| [ner_ade_healthcare](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_healthcare_en.html)  |\n","| 13| [ner_anatomy](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_en.html)  | 14| [ner_anatomy_coarse](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_coarse_en.html)  | 15| [ner_anatomy_coarse_en](https://nlp.johnsnowlabs.com/2020/11/04/ner_anatomy_coarse_en.html)  | 16| [ner_anatomy_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/05/15/ner_anatomy_emb_clinical_large_en.html)  |\n","| 17| [ner_anatomy_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/05/15/ner_anatomy_emb_clinical_medium_en.html)  | 18| [ner_anatomy_en](https://nlp.johnsnowlabs.com/2020/04/22/ner_anatomy_en.html)  | 19| [ner_aspect_based_sentiment](https://nlp.johnsnowlabs.com/2021/03/31/ner_aspect_based_sentiment_en.html)  | 20| [ner_bacterial_species](https://nlp.johnsnowlabs.com/2021/04/01/ner_bacterial_species_en.html)  |\n","| 21| [ner_bacterial_species_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/05/23/ner_bacterial_species_emb_clinical_large_en.html)  | 22| [ner_bacterial_species_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/05/23/ner_bacterial_species_emb_clinical_medium_en.html)  | 23| [ner_bacterial_species_langtest](https://nlp.johnsnowlabs.com/2023/10/15/ner_bacterial_species_langtest_en.html)  | 24| [ner_biomarker](https://nlp.johnsnowlabs.com/2021/11/26/ner_biomarker_en.html)  |\n","| 25| [ner_biomarker_langtest](https://nlp.johnsnowlabs.com/2023/10/10/ner_biomarker_langtest_en.html)  | 26| [ner_biomedical_bc2gm](https://nlp.johnsnowlabs.com/2022/05/11/ner_biomedical_bc2gm_en_2_4.html)  | 27| [ner_bionlp](https://nlp.johnsnowlabs.com/2021/03/31/ner_bionlp_en.html)  | 28| [ner_bionlp_en](https://nlp.johnsnowlabs.com/2020/01/30/ner_bionlp_en.html)  |\n","| 29| [ner_bionlp_langtest](https://nlp.johnsnowlabs.com/2023/10/10/ner_bionlp_langtest_en.html)  | 30| [ner_cancer_genetics](https://nlp.johnsnowlabs.com/2021/03/31/ner_cancer_genetics_en.html)  | 31| [ner_cellular](https://nlp.johnsnowlabs.com/2021/03/31/ner_cellular_en.html)  | 32| [ner_cellular_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/05/24/ner_cellular_emb_clinical_large_en.html)  |\n","| 33| [ner_cellular_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/05/24/ner_cellular_emb_clinical_medium_en.html)  | 34| [ner_cellular_en](https://nlp.johnsnowlabs.com/2020/04/22/ner_cellular_en.html)  | 35| [ner_cellular_langtest](https://nlp.johnsnowlabs.com/2023/10/15/ner_cellular_langtest_en.html)  | 36| [ner_chemd_clinical](https://nlp.johnsnowlabs.com/2021/11/04/ner_chemd_clinical_en.html)  |\n","| 37| [ner_chemicals](https://nlp.johnsnowlabs.com/2021/04/01/ner_chemicals_en.html)  | 38| [ner_chemicals_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/06/02/ner_chemicals_emb_clinical_large_en.html)  | 39| [ner_chemicals_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/02/ner_chemicals_emb_clinical_medium_en.html)  | 40| [ner_chemprot_clinical](https://nlp.johnsnowlabs.com/2020/09/21/ner_chemprot_clinical_en.html)  |\n","| 41| [ner_chemprot_clinical_langtest](https://nlp.johnsnowlabs.com/2023/10/12/ner_chemprot_clinical_langtest_en.html)  | 42| [ner_chexpert](https://nlp.johnsnowlabs.com/2021/09/30/ner_chexpert_en.html)  | 43| [ner_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_clinical_en.html)  | 44| [ner_clinical_abbreviation_langtest](https://nlp.johnsnowlabs.com/2024/01/25/ner_clinical_abbreviation_langtest_en.html)  |\n","| 45| [ner_clinical_en](https://nlp.johnsnowlabs.com/2020/01/30/ner_clinical_en.html)  | 46| [ner_clinical_langtest](https://nlp.johnsnowlabs.com/2023/09/26/ner_clinical_langtest_en.html)  | 47| [ner_clinical_large](https://nlp.johnsnowlabs.com/2021/03/31/ner_clinical_large_en.html)  | 48| [ner_clinical_large_en](https://nlp.johnsnowlabs.com/2020/05/23/ner_clinical_large_en.html)  |\n","| 49| [ner_clinical_large_langtest](https://nlp.johnsnowlabs.com/2023/10/10/ner_clinical_large_langtest_en.html)  | 50| [ner_clinical_trials_abstracts](https://nlp.johnsnowlabs.com/2022/06/22/ner_clinical_trials_abstracts_en_3_0.html)  | 51| [ner_covid_trials](https://nlp.johnsnowlabs.com/2022/10/19/ner_covid_trials_en.html)  | 52| [ner_crf](https://nlp.johnsnowlabs.com/2020/01/28/ner_crf_en.html)  |\n","| 53| [ner_deid_enriched_langtest](https://nlp.johnsnowlabs.com/2023/10/15/ner_deid_enriched_langtest_en.html)  | 54| [ner_deid_generic_augmented_allUpperCased_langtest](https://nlp.johnsnowlabs.com/2023/10/16/ner_deid_generic_augmented_allUpperCased_langtest_en.html)  | 55| [ner_deid_generic_augmented_langtest](https://nlp.johnsnowlabs.com/2023/09/26/ner_deid_generic_augmented_langtest_en.html)  | 56| [ner_deid_large_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/04/12/ner_deid_large_emb_clinical_large_en.html)  |\n","| 57| [ner_deid_large_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/04/12/ner_deid_large_emb_clinical_medium_en.html)  | 58| [ner_deid_large_langtest](https://nlp.johnsnowlabs.com/2023/10/15/ner_deid_large_langtest_en.html)  | 59| [ner_deid_subentity_augmented](https://nlp.johnsnowlabs.com/2021/09/03/ner_deid_subentity_augmented_en.html)  | 60| [ner_deid_subentity_augmented_langtest](https://nlp.johnsnowlabs.com/2023/09/26/ner_deid_subentity_augmented_langtest_en.html)  |\n","| 61| [ner_demographic_extended_healthcare](https://nlp.johnsnowlabs.com/2023/06/08/ner_demographic_extended_healthcare_en.html)  | 62| [ner_diseases](https://nlp.johnsnowlabs.com/2021/03/31/ner_diseases_en.html)  | 63| [ner_diseases_en](https://nlp.johnsnowlabs.com/2020/03/25/ner_diseases_en.html)  | 64| [ner_diseases_langtest](https://nlp.johnsnowlabs.com/2023/10/15/ner_diseases_langtest_en.html)  |\n","| 65| [ner_diseases_large](https://nlp.johnsnowlabs.com/2021/04/01/ner_diseases_large_en.html)  | 66| [ner_drugprot_clinical](https://nlp.johnsnowlabs.com/2021/12/20/ner_drugprot_clinical_en.html)  | 67| [ner_drugs](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_en.html)  | 68| [ner_drugs_en](https://nlp.johnsnowlabs.com/2020/03/25/ner_drugs_en.html)  |\n","| 69| [ner_drugs_greedy](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_greedy_en.html)  | 70| [ner_drugs_large](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_large_en.html)  | 71| [ner_drugs_large_en](https://nlp.johnsnowlabs.com/2021/01/29/ner_drugs_large_en.html)  | 72| [ner_eu_clinical_case](https://nlp.johnsnowlabs.com/2023/01/25/ner_eu_clinical_case_en.html)  |\n","| 73| [ner_eu_clinical_condition](https://nlp.johnsnowlabs.com/2023/02/06/ner_eu_clinical_condition_en.html)  | 74| [ner_eu_clinical_condition_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_eu_clinical_condition_langtest_en.html)  | 75| [ner_events_admission_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_events_admission_clinical_en.html)  | 76| [ner_events_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_events_clinical_en.html)  |\n","| 77| [ner_events_clinical_langtest](https://nlp.johnsnowlabs.com/2023/08/31/ner_events_clinical_langtest_en.html)  | 78| [ner_events_healthcare](https://nlp.johnsnowlabs.com/2021/04/01/ner_events_healthcare_en.html)  | 79| [ner_financial_contract](https://nlp.johnsnowlabs.com/2021/04/01/ner_financial_contract_en.html)  | 80| [ner_genetic_variants](https://nlp.johnsnowlabs.com/2021/06/25/ner_genetic_variants_en.html)  |\n","| 81| [ner_healthcare](https://nlp.johnsnowlabs.com/2021/04/21/ner_healthcare_en.html)  | 82| [ner_healthcare_en](https://nlp.johnsnowlabs.com/2020/03/26/ner_healthcare_en.html)  | 83| [ner_human_phenotype_gene_clinical](https://nlp.johnsnowlabs.com/2020/09/21/ner_human_phenotype_gene_clinical_en.html)  | 84| [ner_human_phenotype_gene_clinical_langtest](https://nlp.johnsnowlabs.com/2023/11/04/ner_human_phenotype_gene_clinical_langtest_en.html)  |\n","| 85| [ner_human_phenotype_go_clinical](https://nlp.johnsnowlabs.com/2020/09/21/ner_human_phenotype_go_clinical_en.html)  | 86| [ner_human_phenotype_go_clinical_langtest](https://nlp.johnsnowlabs.com/2023/11/04/ner_human_phenotype_go_clinical_langtest_en.html)  | 87| [ner_jsl](https://nlp.johnsnowlabs.com/2022/10/19/ner_jsl_en.html)  | 88| [ner_jsl_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/04/12/ner_jsl_emb_clinical_large_en.html)  |\n","| 89| [ner_jsl_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/04/12/ner_jsl_emb_clinical_medium_en.html)  | 90| [ner_jsl_en](https://nlp.johnsnowlabs.com/2020/04/22/ner_jsl_en.html)  | 91| [ner_jsl_enriched](https://nlp.johnsnowlabs.com/2021/10/22/ner_jsl_enriched_en.html)  | 92| [ner_jsl_enriched_en](https://nlp.johnsnowlabs.com/2020/04/22/ner_jsl_enriched_en.html)  |\n","| 93| [ner_jsl_greedy](https://nlp.johnsnowlabs.com/2021/06/24/ner_jsl_greedy_en.html)  | 94| [ner_jsl_langtest](https://nlp.johnsnowlabs.com/2023/07/31/ner_jsl_langtest_en.html)  | 95| [ner_jsl_limited_80p_for_benchmarks](https://nlp.johnsnowlabs.com/2023/04/02/ner_jsl_limited_80p_for_benchmarks_en.html)  | 96| [ner_jsl_slim](https://nlp.johnsnowlabs.com/2021/08/13/ner_jsl_slim_en.html)  |\n","| 97| [ner_living_species](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_en_3_0.html)  | 98| [ner_living_species_langtest](https://nlp.johnsnowlabs.com/2023/10/10/ner_living_species_langtest_en.html)  | 99| [ner_measurements_clinical](https://nlp.johnsnowlabs.com/2021/04/01/ner_measurements_clinical_en.html)  | 100| [ner_medmentions_coarse](https://nlp.johnsnowlabs.com/2021/04/01/ner_medmentions_coarse_en.html)  |\n","| 101| [ner_nature_nero_clinical](https://nlp.johnsnowlabs.com/2022/02/08/ner_nature_nero_clinical_en.html)  | 102| [ner_nihss](https://nlp.johnsnowlabs.com/2021/11/15/ner_nihss_en.html)  | 103| [ner_oncology](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_en.html)  | 104| [ner_oncology_anatomy_general](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_anatomy_general_en.html)  |\n","| 105| [ner_oncology_anatomy_general_healthcare](https://nlp.johnsnowlabs.com/2023/01/11/ner_oncology_anatomy_general_healthcare_en.html)  | 106| [ner_oncology_anatomy_general_langtest](https://nlp.johnsnowlabs.com/2023/09/03/ner_oncology_anatomy_general_langtest_en.html)  | 107| [ner_oncology_anatomy_general_wip](https://nlp.johnsnowlabs.com/2022/09/30/ner_oncology_anatomy_general_wip_en.html)  | 108| [ner_oncology_anatomy_granular](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_anatomy_granular_en.html)  |\n","| 109| [ner_oncology_anatomy_granular_langtest](https://nlp.johnsnowlabs.com/2023/09/03/ner_oncology_anatomy_granular_langtest_en.html)  | 110| [ner_oncology_anatomy_granular_wip](https://nlp.johnsnowlabs.com/2022/10/01/ner_oncology_anatomy_granular_wip_en.html)  | 111| [ner_oncology_biomarker](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_biomarker_en.html)  | 112| [ner_oncology_biomarker_healthcare](https://nlp.johnsnowlabs.com/2023/01/11/ner_oncology_biomarker_healthcare_en.html)  |\n","| 113| [ner_oncology_biomarker_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_oncology_biomarker_langtest_en.html)  | 114| [ner_oncology_biomarker_wip](https://nlp.johnsnowlabs.com/2022/10/01/ner_oncology_biomarker_wip_en.html)  | 115| [ner_oncology_demographics](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_demographics_en.html)  | 116| [ner_oncology_demographics_langtest](https://nlp.johnsnowlabs.com/2023/09/03/ner_oncology_demographics_langtest_en.html)  |\n","| 117| [ner_oncology_demographics_wip](https://nlp.johnsnowlabs.com/2022/09/30/ner_oncology_demographics_wip_en.html)  | 118| [ner_oncology_diagnosis](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_diagnosis_en.html)  | 119| [ner_oncology_diagnosis_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_oncology_diagnosis_langtest_en.html)  | 120| [ner_oncology_diagnosis_wip](https://nlp.johnsnowlabs.com/2022/09/30/ner_oncology_diagnosis_wip_en.html)  |\n","| 121| [ner_oncology_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/04/12/ner_oncology_emb_clinical_large_en.html)  | 122| [ner_oncology_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/04/12/ner_oncology_emb_clinical_medium_en.html)  | 123| [ner_oncology_langtest](https://nlp.johnsnowlabs.com/2023/10/15/ner_oncology_langtest_en.html)  | 124| [ner_oncology_limited_80p_for_benchmarks](https://nlp.johnsnowlabs.com/2023/04/03/ner_oncology_limited_80p_for_benchmarks_en.html)  |\n","| 125| [ner_oncology_posology](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_posology_en.html)  | 126| [ner_oncology_posology_langtest](https://nlp.johnsnowlabs.com/2023/09/04/ner_oncology_posology_langtest_en.html)  | 127| [ner_oncology_posology_wip](https://nlp.johnsnowlabs.com/2022/10/01/ner_oncology_posology_wip_en.html)  | 128| [ner_oncology_response_to_treatment](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_response_to_treatment_en.html)  |\n","| 129| [ner_oncology_response_to_treatment_langtest](https://nlp.johnsnowlabs.com/2023/09/04/ner_oncology_response_to_treatment_langtest_en.html)  | 130| [ner_oncology_response_to_treatment_wip](https://nlp.johnsnowlabs.com/2022/10/01/ner_oncology_response_to_treatment_wip_en.html)  | 131| [ner_oncology_test](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_test_en.html)  | 132| [ner_oncology_test_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_oncology_test_langtest_en.html)  |\n","| 133| [ner_oncology_test_wip](https://nlp.johnsnowlabs.com/2022/09/30/ner_oncology_test_wip_en.html)  | 134| [ner_oncology_therapy](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_therapy_en.html)  | 135| [ner_oncology_therapy_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_oncology_therapy_langtest_en.html)  | 136| [ner_oncology_therapy_wip](https://nlp.johnsnowlabs.com/2022/09/30/ner_oncology_therapy_wip_en.html)  |\n","| 137| [ner_oncology_tnm](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_tnm_en.html)  | 138| [ner_oncology_tnm_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_oncology_tnm_langtest_en.html)  | 139| [ner_oncology_tnm_wip](https://nlp.johnsnowlabs.com/2022/09/30/ner_oncology_tnm_wip_en.html)  | 140| [ner_oncology_unspecific_posology](https://nlp.johnsnowlabs.com/2022/10/25/ner_oncology_unspecific_posology_en.html)  |\n","| 141| [ner_oncology_unspecific_posology_healthcare](https://nlp.johnsnowlabs.com/2023/01/11/ner_oncology_unspecific_posology_healthcare_en.html)  | 142| [ner_oncology_unspecific_posology_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_oncology_unspecific_posology_langtest_en.html)  | 143| [ner_oncology_unspecific_posology_wip](https://nlp.johnsnowlabs.com/2022/09/30/ner_oncology_unspecific_posology_wip_en.html)  | 144| [ner_oncology_wip](https://nlp.johnsnowlabs.com/2022/09/30/ner_oncology_wip_en.html)  |\n","| 145| [ner_opioid](https://nlp.johnsnowlabs.com/2024/02/28/ner_opioid_en.html)  | 146| [ner_opioid_small_wip](https://nlp.johnsnowlabs.com/2024/01/11/ner_opioid_small_wip_en.html)  | 147| [ner_pathogen](https://nlp.johnsnowlabs.com/2022/06/28/ner_pathogen_en_3_0.html)  | 148| [ner_posology](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_en.html)  |\n","| 149| [ner_posology_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/04/12/ner_posology_emb_clinical_large_en.html)  | 150| [ner_posology_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/04/12/ner_posology_emb_clinical_medium_en.html)  | 151| [ner_posology_en](https://nlp.johnsnowlabs.com/2020/04/15/ner_posology_en.html)  | 152| [ner_posology_experimental](https://nlp.johnsnowlabs.com/2021/09/01/ner_posology_experimental_en.html)  |\n","| 153| [ner_posology_greedy](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_greedy_en.html)  | 154| [ner_posology_healthcare](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_healthcare_en.html)  | 155| [ner_posology_langtest](https://nlp.johnsnowlabs.com/2023/07/28/ner_posology_langtest_en.html)  | 156| [ner_posology_large](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_large_en.html)  |\n","| 157| [ner_posology_small](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_small_en.html)  | 158| [ner_radiology](https://nlp.johnsnowlabs.com/2021/03/31/ner_radiology_en.html)  | 159| [ner_radiology_wip_clinical](https://nlp.johnsnowlabs.com/2021/04/01/ner_radiology_wip_clinical_en.html)  | 160| [ner_risk_factors](https://nlp.johnsnowlabs.com/2021/03/31/ner_risk_factors_en.html)  |\n","| 161| [ner_risk_factors_en](https://nlp.johnsnowlabs.com/2020/04/22/ner_risk_factors_en.html)  | 162| [ner_risk_factors_langtest](https://nlp.johnsnowlabs.com/2023/11/06/ner_risk_factors_langtest_en.html)  | 163| [ner_sdoh](https://nlp.johnsnowlabs.com/2023/06/13/ner_sdoh_en.html)  | 164| [ner_sdoh_access_to_healthcare](https://nlp.johnsnowlabs.com/2023/07/02/ner_sdoh_access_to_healthcare_en.html)  |\n","| 165| [ner_sdoh_access_to_healthcare_wip](https://nlp.johnsnowlabs.com/2023/02/24/ner_sdoh_access_to_healthcare_wip_en.html)  | 166| [ner_sdoh_community_condition](https://nlp.johnsnowlabs.com/2023/07/02/ner_sdoh_community_condition_en.html)  | 167| [ner_sdoh_community_condition_wip](https://nlp.johnsnowlabs.com/2023/02/24/ner_sdoh_community_condition_wip_en.html)  | 168| [ner_sdoh_demographics](https://nlp.johnsnowlabs.com/2023/07/02/ner_sdoh_demographics_en.html)  |\n","| 169| [ner_sdoh_demographics_wip](https://nlp.johnsnowlabs.com/2023/02/10/ner_sdoh_demographics_wip_en.html)  | 170| [ner_sdoh_emb_clinical_large_wip](https://nlp.johnsnowlabs.com/2023/04/17/ner_sdoh_emb_clinical_large_wip_en.html)  | 171| [ner_sdoh_emb_clinical_medium_wip](https://nlp.johnsnowlabs.com/2023/04/27/ner_sdoh_emb_clinical_medium_wip_en.html)  | 172| [ner_sdoh_health_behaviours_problems](https://nlp.johnsnowlabs.com/2023/07/02/ner_sdoh_health_behaviours_problems_en.html)  |\n","| 173| [ner_sdoh_health_behaviours_problems_wip](https://nlp.johnsnowlabs.com/2023/02/24/ner_sdoh_health_behaviours_problems_wip_en.html)  | 174| [ner_sdoh_income_social_status](https://nlp.johnsnowlabs.com/2023/07/02/ner_sdoh_income_social_status_en.html)  | 175| [ner_sdoh_income_social_status_wip](https://nlp.johnsnowlabs.com/2023/02/10/ner_sdoh_income_social_status_wip_en.html)  | 176| [ner_sdoh_langtest](https://nlp.johnsnowlabs.com/2023/07/31/ner_sdoh_langtest_en.html)  |\n","| 177| [ner_sdoh_mentions](https://nlp.johnsnowlabs.com/2022/12/18/ner_sdoh_mentions_en.html)  | 178| [ner_sdoh_slim_wip](https://nlp.johnsnowlabs.com/2022/11/15/ner_sdoh_slim_wip_en.html)  | 179| [ner_sdoh_social_environment](https://nlp.johnsnowlabs.com/2023/07/02/ner_sdoh_social_environment_en.html)  | 180| [ner_sdoh_social_environment_wip](https://nlp.johnsnowlabs.com/2023/02/10/ner_sdoh_social_environment_wip_en.html)  |\n","| 181| [ner_sdoh_substance_usage](https://nlp.johnsnowlabs.com/2023/07/02/ner_sdoh_substance_usage_en.html)  | 182| [ner_sdoh_substance_usage_wip](https://nlp.johnsnowlabs.com/2023/02/23/ner_sdoh_substance_usage_wip_en.html)  | 183| [ner_sdoh_wip](https://nlp.johnsnowlabs.com/2023/02/11/ner_sdoh_wip_en.html)  | 184| [ner_section_header_diagnosis](https://nlp.johnsnowlabs.com/2023/07/26/ner_section_header_diagnosis_en.html)  |\n","| 185| [ner_snomed_term](https://nlp.johnsnowlabs.com/2024/02/13/ner_snomed_term_en.html)  | 186| [ner_supplement_clinical](https://nlp.johnsnowlabs.com/2022/02/01/ner_supplement_clinical_en.html)  | 187| [ner_vop](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_en.html)  | 188| [ner_vop_anatomy](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_anatomy_en.html)  |\n","| 189| [ner_vop_anatomy_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_anatomy_emb_clinical_large_en.html)  | 190| [ner_vop_anatomy_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/07/ner_vop_anatomy_emb_clinical_medium_en.html)  | 191| [ner_vop_anatomy_langtest](https://nlp.johnsnowlabs.com/2023/09/21/ner_vop_anatomy_langtest_en.html)  | 192| [ner_vop_anatomy_wip](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_anatomy_wip_en.html)  |\n","| 193| [ner_vop_clinical_dept](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_clinical_dept_en.html)  | 194| [ner_vop_clinical_dept_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_clinical_dept_emb_clinical_large_en.html)  | 195| [ner_vop_clinical_dept_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_clinical_dept_emb_clinical_medium_en.html)  | 196| [ner_vop_clinical_dept_langtest](https://nlp.johnsnowlabs.com/2023/09/21/ner_vop_clinical_dept_langtest_en.html)  |\n","| 197| [ner_vop_clinical_dept_wip](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_clinical_dept_wip_en.html)  | 198| [ner_vop_demographic](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_demographic_en.html)  | 199| [ner_vop_demographic_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_demographic_emb_clinical_large_en.html)  | 200| [ner_vop_demographic_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_demographic_emb_clinical_medium_en.html)  |\n","| 201| [ner_vop_demographic_langtest](https://nlp.johnsnowlabs.com/2023/09/21/ner_vop_demographic_langtest_en.html)  | 202| [ner_vop_demographic_wip](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_demographic_wip_en.html)  | 203| [ner_vop_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_emb_clinical_large_en.html)  | 204| [ner_vop_emb_clinical_large_wip](https://nlp.johnsnowlabs.com/2023/04/12/ner_vop_emb_clinical_large_wip_en.html)  |\n","| 205| [ner_vop_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_emb_clinical_medium_en.html)  | 206| [ner_vop_emb_clinical_medium_wip](https://nlp.johnsnowlabs.com/2023/04/12/ner_vop_emb_clinical_medium_wip_en.html)  | 207| [ner_vop_langtest](https://nlp.johnsnowlabs.com/2023/10/10/ner_vop_langtest_en.html)  | 208| [ner_vop_problem](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_problem_en.html)  |\n","| 209| [ner_vop_problem_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_problem_emb_clinical_large_en.html)  | 210| [ner_vop_problem_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_problem_emb_clinical_medium_en.html)  | 211| [ner_vop_problem_langtest](https://nlp.johnsnowlabs.com/2023/09/21/ner_vop_problem_langtest_en.html)  | 212| [ner_vop_problem_reduced](https://nlp.johnsnowlabs.com/2023/06/07/ner_vop_problem_reduced_en.html)  |\n","| 213| [ner_vop_problem_reduced_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/06/07/ner_vop_problem_reduced_emb_clinical_large_en.html)  | 214| [ner_vop_problem_reduced_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/07/ner_vop_problem_reduced_emb_clinical_medium_en.html)  | 215| [ner_vop_problem_reduced_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_vop_problem_reduced_langtest_en.html)  | 216| [ner_vop_problem_reduced_wip](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_problem_reduced_wip_en.html)  |\n","| 217| [ner_vop_problem_wip](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_problem_wip_en.html)  | 218| [ner_vop_slim_wip](https://nlp.johnsnowlabs.com/2023/02/25/ner_vop_slim_wip_en.html)  | 219| [ner_vop_temporal](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_temporal_en.html)  | 220| [ner_vop_temporal_emb_clinical_large_final](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_temporal_emb_clinical_large_final_en.html)  |\n","| 221| [ner_vop_temporal_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_temporal_emb_clinical_medium_en.html)  | 222| [ner_vop_temporal_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_vop_temporal_langtest_en.html)  | 223| [ner_vop_temporal_wip](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_temporal_wip_en.html)  | 224| [ner_vop_test](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_test_en.html)  |\n","| 225| [ner_vop_test_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_test_emb_clinical_large_en.html)  | 226| [ner_vop_test_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_test_emb_clinical_medium_en.html)  | 227| [ner_vop_test_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_vop_test_langtest_en.html)  | 228| [ner_vop_test_wip](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_test_wip_en.html)  |\n","| 229| [ner_vop_treatment](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_treatment_en.html)  | 230| [ner_vop_treatment_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_treatment_emb_clinical_large_en.html)  | 231| [ner_vop_treatment_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/06/06/ner_vop_treatment_emb_clinical_medium_en.html)  | 232| [ner_vop_treatment_langtest](https://nlp.johnsnowlabs.com/2023/09/22/ner_vop_treatment_langtest_en.html)  |\n","| 233| [ner_vop_treatment_wip](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_treatment_wip_en.html)  | 234| [ner_vop_wip](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_wip_en.html)  | 235| [ner_vop_wip_emb_clinical_large](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_wip_embeddings_clinical_large_en.html)  | 236| [ner_vop_wip_emb_clinical_medium](https://nlp.johnsnowlabs.com/2023/05/19/ner_vop_wip_embeddings_clinical_medium_en.html)  |\n","| 237| [nerdl_tumour_demo](https://nlp.johnsnowlabs.com/2021/04/01/nerdl_tumour_demo_en.html)  |\n","\n","\n","\n","\n","- **Multilingual Clinical NER Models**\n","\n","|index|model|index|model|index|model|index|model|\n","|-----:|:-----|-----:|:-----|-----:|:-----|-----:|:-----|\n","| 1| [ner_clinical](https://nlp.johnsnowlabs.com/2023/09/20/ner_clinical_sv.html)  | 2| [ner_clinical_bert](https://nlp.johnsnowlabs.com/2022/11/22/ner_clinical_bert_ro.html)  | 3| [ner_clinical_trials_abstracts](https://nlp.johnsnowlabs.com/2022/08/12/ner_clinical_trials_abstracts_es_3_0.html)  | 4| [ner_deid_generic](https://nlp.johnsnowlabs.com/2023/05/30/ner_deid_generic_ar.html)  |\n","| 5| [ner_deid_generic_arabert](https://nlp.johnsnowlabs.com/2023/09/16/ner_deid_generic_arabert_ar.html)  | 6| [ner_deid_generic_augmented](https://nlp.johnsnowlabs.com/2022/02/16/ner_deid_generic_augmented_es.html)  | 7| [ner_deid_generic_bert](https://nlp.johnsnowlabs.com/2022/11/22/ner_deid_generic_bert_ro.html)  | 8| [ner_deid_generic_camelbert](https://nlp.johnsnowlabs.com/2023/09/16/ner_deid_generic_camelbert_ar.html)  |\n","| 9| [ner_deid_generic_roberta](https://nlp.johnsnowlabs.com/2022/01/17/ner_deid_generic_roberta_es.html)  | 10| [ner_deid_generic_roberta_augmented](https://nlp.johnsnowlabs.com/2022/02/16/ner_deid_generic_roberta_augmented_es.html)  | 11| [ner_deid_multilingual](https://nlp.johnsnowlabs.com/2024/02/12/ner_deid_multilingual_xx.html)  | 12| [ner_deid_name_multilingual](https://nlp.johnsnowlabs.com/2024/01/17/ner_deid_name_multilingual_xx.html)  |\n","| 13| [ner_deid_name_multilingual_clinical](https://nlp.johnsnowlabs.com/2024/02/12/ner_deid_name_multilingual_clinical_xx.html)  | 14| [ner_deid_subentity](https://nlp.johnsnowlabs.com/2023/05/31/ner_deid_subentity_ar.html)  | 15| [ner_deid_subentity_arabert](https://nlp.johnsnowlabs.com/2023/09/16/ner_deid_subentity_arabert_ar.html)  | 16| [ner_deid_subentity_augmented](https://nlp.johnsnowlabs.com/2022/02/16/ner_deid_subentity_augmented_es.html)  |\n","| 17| [ner_deid_subentity_bert](https://nlp.johnsnowlabs.com/2022/06/27/ner_deid_bert_subentity_ro_3_0.html)  | 18| [ner_deid_subentity_camelbert](https://nlp.johnsnowlabs.com/2023/09/22/ner_deid_subentity_camelbert_ar.html)  | 19| [ner_deid_subentity_roberta](https://nlp.johnsnowlabs.com/2022/01/17/ner_deid_subentity_roberta_es.html)  | 20| [ner_deid_subentity_roberta_augmented](https://nlp.johnsnowlabs.com/2022/02/16/ner_deid_subentity_roberta_augmented_es.html)  |\n","| 21| [ner_diag_proc](https://nlp.johnsnowlabs.com/2021/03/31/ner_diag_proc_es.html)  | 22| [ner_eu_clinical_case](https://nlp.johnsnowlabs.com/2023/02/02/ner_eu_clinical_case_eu.html)  | 23| [ner_eu_clinical_condition](https://nlp.johnsnowlabs.com/2023/02/06/ner_eu_clinical_condition_it.html)  | 24| [ner_living_species](https://nlp.johnsnowlabs.com/2022/06/23/ner_living_species_ca_3_0.html)  |\n","| 25| [ner_living_species_300](https://nlp.johnsnowlabs.com/2022/11/22/ner_living_species_300_es.html)  | 26| [ner_living_species_bert](https://nlp.johnsnowlabs.com/2022/06/23/ner_living_species_bert_ro_3_0.html)  | 27| [ner_living_species_roberta](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_roberta_pt_3_0.html)  | 28| [ner_negation_uncertainty](https://nlp.johnsnowlabs.com/2022/08/13/ner_negation_uncertainty_es_3_0.html)  |\n","| 29| [ner_neoplasms](https://nlp.johnsnowlabs.com/2021/03/31/ner_neoplasms_es.html)  | 30| [ner_pharmacology](https://nlp.johnsnowlabs.com/2022/08/13/ner_pharmacology_es_3_0.html)  | 31| [ner_traffic](https://nlp.johnsnowlabs.com/2021/04/01/ner_traffic_de.html)  | 32| [roberta_ner_diag_proc](https://nlp.johnsnowlabs.com/2021/11/04/roberta_ner_diag_proc_es.html)  |\n","\n","- **BioBert NER Models**\n","\n","|index|model|index|model|index|model|index|model|\n","|-----:|:-----|-----:|:-----|-----:|:-----|-----:|:-----|\n","| 1| [jsl_ner_wip_greedy_biobert](https://nlp.johnsnowlabs.com/2021/07/26/jsl_ner_wip_greedy_biobert_en.html)  | 2| [jsl_rd_ner_wip_greedy_biobert](https://nlp.johnsnowlabs.com/2021/07/26/jsl_rd_ner_wip_greedy_biobert_en.html)  | 3| [ner_ade_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_biobert_en.html)  | 4| [ner_anatomy_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_anatomy_biobert_en.html)  |\n","| 5| [ner_anatomy_coarse_biobert](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_coarse_biobert_en.html)  | 6| [ner_anatomy_coarse_biobert_en](https://nlp.johnsnowlabs.com/2020/11/04/ner_anatomy_coarse_biobert_en.html)  | 7| [ner_bionlp_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_bionlp_biobert_en.html)  | 8| [ner_cellular_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_cellular_biobert_en.html)  |\n","| 9| [ner_chemprot_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_chemprot_biobert_en.html)  | 10| [ner_clinical_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_clinical_biobert_en.html)  | 11| [ner_diseases_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_diseases_biobert_en.html)  | 12| [ner_events_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_events_biobert_en.html)  |\n","| 13| [ner_human_phenotype_gene_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_gene_biobert_en.html)  | 14| [ner_human_phenotype_go_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_go_biobert_en.html)  | 15| [ner_jsl_biobert](https://nlp.johnsnowlabs.com/2021/09/05/ner_jsl_biobert_en.html)  | 16| [ner_jsl_enriched_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_jsl_enriched_biobert_en.html)  |\n","| 17| [ner_jsl_greedy_biobert](https://nlp.johnsnowlabs.com/2021/08/13/ner_jsl_greedy_biobert_en.html)  | 18| [ner_living_species_biobert](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_biobert_en_3_0.html)  | 19| [ner_posology_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_biobert_en.html)  | 20| [ner_posology_large_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_large_biobert_en.html)  |\n","| 21| [ner_profiling_biobert](https://nlp.johnsnowlabs.com/2023/06/17/ner_profiling_biobert_en.html)  | 22| [ner_risk_factors_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_risk_factors_biobert_en.html)  |\n","\n","\n","- **BertForTokenClassification Clinical NER models**\n","\n","|index|model|index|model|index|model|index|model|\n","|-----:|:-----|-----:|:-----|-----:|:-----|-----:|:-----|\n","| 1| [bert_token_classifier_ade_tweet_binary](https://nlp.johnsnowlabs.com/2022/07/29/bert_token_classifier_ade_tweet_binary_en_3_0.html)  | 2| [bert_token_classifier_disease_mentions_tweet](https://nlp.johnsnowlabs.com/2022/07/28/bert_token_classifier_disease_mentions_tweet_es_3_0.html)  | 3| [bert_token_classifier_drug_development_trials](https://nlp.johnsnowlabs.com/2022/03/22/bert_token_classifier_drug_development_trials_en_3_0.html)  | 4| [bert_token_classifier_negation_uncertainty](https://nlp.johnsnowlabs.com/2022/08/11/bert_token_classifier_negation_uncertainty_es_3_0.html)  |\n","| 5| [bert_token_classifier_ner_ade](https://nlp.johnsnowlabs.com/2021/09/30/bert_token_classifier_ner_ade_en.html)  | 6| [bert_token_classifier_ner_ade_binary](https://nlp.johnsnowlabs.com/2022/07/27/bert_token_classifier_ner_ade_binary_en_3_0.html)  | 7| [bert_token_classifier_ner_anatem](https://nlp.johnsnowlabs.com/2022/07/25/bert_token_classifier_ner_anatem_en_3_0.html)  | 8| [bert_token_classifier_ner_anatomy](https://nlp.johnsnowlabs.com/2021/09/30/bert_token_classifier_ner_anatomy_en.html)  |\n","| 9| [bert_token_classifier_ner_bacteria](https://nlp.johnsnowlabs.com/2021/09/30/bert_token_classifier_ner_bacteria_en.html)  | 10| [bert_token_classifier_ner_bc2gm_gene](https://nlp.johnsnowlabs.com/2022/07/25/bert_token_classifier_ner_bc2gm_gene_en_3_0.html)  | 11| [bert_token_classifier_ner_bc4chemd_chemicals](https://nlp.johnsnowlabs.com/2022/07/25/bert_token_classifier_ner_bc4chemd_chemicals_en_3_0.html)  | 12| [bert_token_classifier_ner_bc5cdr_chemicals](https://nlp.johnsnowlabs.com/2022/07/25/bert_token_classifier_ner_bc5cdr_chemicals_en_3_0.html)  |\n","| 13| [bert_token_classifier_ner_bc5cdr_disease](https://nlp.johnsnowlabs.com/2022/07/25/bert_token_classifier_ner_bc5cdr_disease_en_3_0.html)  | 14| [bert_token_classifier_ner_bionlp](https://nlp.johnsnowlabs.com/2021/11/03/bert_token_classifier_ner_bionlp_en.html)  | 15| [bert_token_classifier_ner_cellular](https://nlp.johnsnowlabs.com/2021/11/03/bert_token_classifier_ner_cellular_en.html)  | 16| [bert_token_classifier_ner_chemicals](https://nlp.johnsnowlabs.com/2021/10/19/bert_token_classifier_ner_chemicals_en.html)  |\n","| 17| [bert_token_classifier_ner_chemprot](https://nlp.johnsnowlabs.com/2021/10/19/bert_token_classifier_ner_chemprot_en.html)  | 18| [bert_token_classifier_ner_clinical](https://nlp.johnsnowlabs.com/2023/07/05/bert_token_classifier_ner_clinical_nl.html)  | 19| [bert_token_classifier_ner_clinical_trials_abstracts](https://nlp.johnsnowlabs.com/2022/06/29/bert_token_classifier_ner_clinical_trials_abstracts_en_3_0.html)  | 20| [bert_token_classifier_ner_deid](https://nlp.johnsnowlabs.com/2021/09/13/bert_token_classifier_ner_deid_en.html)  |\n","| 21| [bert_token_classifier_ner_drugs](https://nlp.johnsnowlabs.com/2021/09/20/bert_token_classifier_ner_drugs_en.html)  | 22| [bert_token_classifier_ner_jnlpba_cellular](https://nlp.johnsnowlabs.com/2022/07/25/bert_token_classifier_ner_jnlpba_cellular_en_3_0.html)  | 23| [bert_token_classifier_ner_jsl](https://nlp.johnsnowlabs.com/2021/09/16/bert_token_classifier_ner_jsl_en.html)  | 24| [bert_token_classifier_ner_jsl_slim](https://nlp.johnsnowlabs.com/2021/09/24/bert_token_classifier_ner_jsl_slim_en.html)  |\n","| 25| [bert_token_classifier_ner_linnaeus_species](https://nlp.johnsnowlabs.com/2022/07/25/bert_token_classifier_ner_linnaeus_species_en_3_0.html)  | 26| [bert_token_classifier_ner_living_species](https://nlp.johnsnowlabs.com/2022/06/27/bert_token_classifier_ner_living_species_it_3_0.html)  | 27| [bert_token_classifier_ner_ncbi_disease](https://nlp.johnsnowlabs.com/2022/07/25/bert_token_classifier_ner_ncbi_disease_en_3_0.html)  | 28| [bert_token_classifier_ner_pathogen](https://nlp.johnsnowlabs.com/2022/07/28/bert_token_classifier_ner_pathogen_en_3_0.html)  |\n","| 29| [bert_token_classifier_ner_species](https://nlp.johnsnowlabs.com/2022/07/25/bert_token_classifier_ner_species_en_3_0.html)  | 30| [bert_token_classifier_ner_supplement](https://nlp.johnsnowlabs.com/2022/02/09/bert_token_classifier_ner_supplement_en.html)  | 31| [bert_token_classifier_pharmacology](https://nlp.johnsnowlabs.com/2022/08/11/bert_token_classifier_pharmacology_es_3_0.html)  |\n","\n","\n","\n","\n","\n","**You can find all these models and more [NLP Models Hub](https://nlp.johnsnowlabs.com/models?task=Named+Entity+Recognition&edition=Spark+NLP+for+Healthcare)**"]},{"cell_type":"markdown","source":["- **Multilingual Clinical NER Models**\n","\n","|index|model_name|supported_languages||index|model_name|supported_languages|\n","|-----:|:---------|:------------------||-----:|:---------|:------------------|\n","| 1 | [ner_clinical](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html) |[ro](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [de](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [nl](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [pt](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [pl](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [tr](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [es](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [fr](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [it](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [da](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [sv](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [no](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [ja](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [vi](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [ar](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [bg](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [fi](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html), [he](https://nlp.johnsnowlabs.com/2022/07/01/ner_clinical_ro_3_0.html) || 2 | [ner_neoplasms](https://nlp.johnsnowlabs.com/2020/07/08/ner_neoplasms_es.html) |[es](https://nlp.johnsnowlabs.com/2020/07/08/ner_neoplasms_es.html) |\n","| 3 | [ner_healthcare](https://nlp.johnsnowlabs.com/2020/09/28/ner_healthcare_de.html) |[de](https://nlp.johnsnowlabs.com/2020/09/28/ner_healthcare_de.html), [de](https://nlp.johnsnowlabs.com/2020/09/28/ner_healthcare_de.html) || 4 | [ner_healthcare_slim](https://nlp.johnsnowlabs.com/2021/04/01/ner_healthcare_slim_de.html) |[de](https://nlp.johnsnowlabs.com/2021/04/01/ner_healthcare_slim_de.html) |\n","| 5 | [ner_traffic](https://nlp.johnsnowlabs.com/2021/04/01/ner_traffic_de.html) |[de](https://nlp.johnsnowlabs.com/2021/04/01/ner_traffic_de.html) || 6 | [cantemist_scielowiki](https://nlp.johnsnowlabs.com/2021/07/23/cantemist_scielowiki_es.html) |[es](https://nlp.johnsnowlabs.com/2021/07/23/cantemist_scielowiki_es.html) |\n","| 7 | [meddroprof_scielowiki](https://nlp.johnsnowlabs.com/2021/07/26/meddroprof_scielowiki_es.html) |[es](https://nlp.johnsnowlabs.com/2021/07/26/meddroprof_scielowiki_es.html) || 8 | [roberta_ner_diag_proc](https://nlp.johnsnowlabs.com/2021/11/04/roberta_ner_diag_proc_es.html) |[es](https://nlp.johnsnowlabs.com/2021/11/04/roberta_ner_diag_proc_es.html) |\n","| 9 | [ner_living_species_bert](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_bert_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_bert_es_3_0.html), [pt](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_bert_es_3_0.html), [fr](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_bert_es_3_0.html), [it](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_bert_es_3_0.html), [ro](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_bert_es_3_0.html) || 10 | [ner_living_species](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_es_3_0.html), [pt](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_es_3_0.html), [ca](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_es_3_0.html), [fr](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_es_3_0.html), [gl](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_es_3_0.html), [it](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_es_3_0.html) |\n","| 11 | [ner_living_species_roberta](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_roberta_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_roberta_es_3_0.html), [pt](https://nlp.johnsnowlabs.com/2022/06/22/ner_living_species_roberta_es_3_0.html) || 12 | [bert_token_classifier_ner_living_species](https://nlp.johnsnowlabs.com/2022/06/27/bert_token_classifier_ner_living_species_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/06/27/bert_token_classifier_ner_living_species_es_3_0.html), [it](https://nlp.johnsnowlabs.com/2022/06/27/bert_token_classifier_ner_living_species_es_3_0.html), [pt](https://nlp.johnsnowlabs.com/2022/06/27/bert_token_classifier_ner_living_species_es_3_0.html) |\n","| 13 | [ner_clinical_bert](https://nlp.johnsnowlabs.com/2022/06/30/ner_clinical_bert_ro_3_0.html) |[ro](https://nlp.johnsnowlabs.com/2022/06/30/ner_clinical_bert_ro_3_0.html) || 14 | [ner_diag_proc](https://nlp.johnsnowlabs.com/2020/07/08/ner_diag_proc_es.html) |[es](https://nlp.johnsnowlabs.com/2020/07/08/ner_diag_proc_es.html) |\n","| 15 | [ner_living_species_300](https://nlp.johnsnowlabs.com/2022/07/26/ner_living_species_300_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/07/26/ner_living_species_300_es_3_0.html) || 16 | [bert_token_classifier_disease_mentions_tweet](https://nlp.johnsnowlabs.com/2022/07/28/bert_token_classifier_disease_mentions_tweet_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/07/28/bert_token_classifier_disease_mentions_tweet_es_3_0.html) |\n","| 17 | [bert_token_classifier_negation_uncertainty](https://nlp.johnsnowlabs.com/2022/08/11/bert_token_classifier_negation_uncertainty_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/08/11/bert_token_classifier_negation_uncertainty_es_3_0.html) || 18 | [bert_token_classifier_ner_clinical_trials_abstracts](https://nlp.johnsnowlabs.com/2022/08/11/bert_token_classifier_ner_clinical_trials_abstracts_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/08/11/bert_token_classifier_ner_clinical_trials_abstracts_es_3_0.html) |\n","| 19 | [bert_token_classifier_pharmacology](https://nlp.johnsnowlabs.com/2022/08/11/bert_token_classifier_pharmacology_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/08/11/bert_token_classifier_pharmacology_es_3_0.html) || 20 | [ner_clinical_trials_abstracts](https://nlp.johnsnowlabs.com/2022/08/12/ner_clinical_trials_abstracts_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/08/12/ner_clinical_trials_abstracts_es_3_0.html) |\n","| 21 | [ner_negation_uncertainty](https://nlp.johnsnowlabs.com/2022/08/13/ner_negation_uncertainty_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/08/13/ner_negation_uncertainty_es_3_0.html) || 22 | [ner_pharmacology](https://nlp.johnsnowlabs.com/2022/08/13/ner_pharmacology_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/08/13/ner_pharmacology_es_3_0.html) |\n","| 23 | [disease_mentions_tweet](https://nlp.johnsnowlabs.com/2022/08/14/disease_mentions_tweet_es_3_0.html) |[es](https://nlp.johnsnowlabs.com/2022/08/14/disease_mentions_tweet_es_3_0.html) || 24 | [ner_eu_clinical_case](https://nlp.johnsnowlabs.com/2023/02/01/ner_eu_clinical_case_es.html) |[es](https://nlp.johnsnowlabs.com/2023/02/01/ner_eu_clinical_case_es.html), [fr](https://nlp.johnsnowlabs.com/2023/02/01/ner_eu_clinical_case_es.html), [eu](https://nlp.johnsnowlabs.com/2023/02/01/ner_eu_clinical_case_es.html) |\n","| 25 | [ner_eu_clinical_condition](https://nlp.johnsnowlabs.com/2023/02/06/ner_eu_clinical_condition_es.html) |[es](https://nlp.johnsnowlabs.com/2023/02/06/ner_eu_clinical_condition_es.html), [eu](https://nlp.johnsnowlabs.com/2023/02/06/ner_eu_clinical_condition_es.html), [fr](https://nlp.johnsnowlabs.com/2023/02/06/ner_eu_clinical_condition_es.html), [it](https://nlp.johnsnowlabs.com/2023/02/06/ner_eu_clinical_condition_es.html) || 26 | [bert_token_classifier_ner_clinical](https://nlp.johnsnowlabs.com/2023/07/05/bert_token_classifier_ner_clinical_nl.html) |[nl](https://nlp.johnsnowlabs.com/2023/07/05/bert_token_classifier_ner_clinical_nl.html) |\n","| 27 | [ner_deid_name_multilingual](https://nlp.johnsnowlabs.com/2024/01/17/ner_deid_name_multilingual_xx.html) |[xx](https://nlp.johnsnowlabs.com/2024/01/17/ner_deid_name_multilingual_xx.html) || 28 | [ner_deid_name_multilingual_clinical](https://nlp.johnsnowlabs.com/2024/02/12/ner_deid_name_multilingual_clinical_xx.html) |[xx](https://nlp.johnsnowlabs.com/2024/02/12/ner_deid_name_multilingual_clinical_xx.html) |\n","| 29 | [ner_deid_name_multilingual_clinical_langtest](https://nlp.johnsnowlabs.com/2024/03/12/ner_deid_name_multilingual_clinical_langtest_xx.html) |[xx](https://nlp.johnsnowlabs.com/2024/03/12/ner_deid_name_multilingual_clinical_langtest_xx.html) |"],"metadata":{"id":"BiptUwfQxxtY"}},{"cell_type":"markdown","metadata":{"id":"BZx93c02IyyP"},"source":["**Also, you can print the list of clinical pretrained models/pipelines and annotators with one-line code:**"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15269,"status":"ok","timestamp":1712934915258,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"4l4VvgI1dRb6","outputId":"dbde0936-2a98-48e4-f3cd-a576187504a0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[['nerdl_tumour_demo', 'en', '1.7.3'],\n"," ['nerdl_tumour_demo', 'en', '1.8.0'],\n"," ['nerdl_tumour_demo', 'en', '2.0.2'],\n"," ['ner_healthcare', 'en', '2.4.4'],\n"," ['ner_radiology', 'en', '2.7.0'],\n"," ['ner_deid_augmented', 'en', '2.7.1'],\n"," ['ner_deidentify_dl', 'en', '2.7.2'],\n"," ['ner_events_admission_clinical', 'en', '2.7.4'],\n"," ['ner_clinical', 'en', '3.0.0'],\n"," ['ner_radiology', 'en', '3.0.0'],\n"," ['ner_bionlp', 'en', '3.0.0'],\n"," ['ner_posology', 'en', '3.0.0'],\n"," ['ner_healthcare', 'de', '3.0.0'],\n"," ['ner_deid_augmented', 'en', '3.0.0'],\n"," ['ner_anatomy', 'en', '3.0.0'],\n"," ['ner_risk_factors', 'en', '3.0.0'],\n"," ['ner_chemprot_clinical', 'en', '3.0.0'],\n"," ['ner_posology_small', 'en', '3.0.0'],\n"," ['ner_diag_proc', 'es', '3.0.0'],\n"," ['ner_posology_greedy', 'en', '3.0.0'],\n"," ['ner_deid_enriched', 'en', '3.0.0'],\n"," ['ner_drugs_greedy', 'en', '3.0.0'],\n"," ['jsl_ner_wip_clinical', 'en', '3.0.0'],\n"," ['ner_posology_large', 'en', '3.0.0'],\n"," ['jsl_ner_wip_greedy_clinical', 'en', '3.0.0'],\n"," ['ner_clinical_large', 'en', '3.0.0'],\n"," ['ner_diseases', 'en', '3.0.0'],\n"," ['ner_aspect_based_sentiment', 'en', '3.0.0'],\n"," ['ner_cellular', 'en', '3.0.0'],\n"," ['ner_drugs', 'en', '3.0.0'],\n"," ['ner_jsl', 'en', '3.0.0'],\n"," ['ner_anatomy_coarse_biobert', 'en', '3.0.0'],\n"," ['ner_cancer_genetics', 'en', '3.0.0'],\n"," ['ner_drugs_large', 'en', '3.0.0'],\n"," ['ner_events_admission_clinical', 'en', '3.0.0'],\n"," ['ner_deidentify_dl', 'en', '3.0.0'],\n"," ['ner_deid_synthetic', 'en', '3.0.0'],\n"," ['ner_jsl_enriched', 'en', '3.0.0'],\n"," ['ner_deid_large', 'en', '3.0.0'],\n"," ['ner_human_phenotype_gene_clinical', 'en', '3.0.0'],\n"," ['ner_human_phenotype_go_clinical', 'en', '3.0.0'],\n"," ['ner_events_clinical', 'en', '3.0.0'],\n"," ['ner_legal', 'de', '3.0.0'],\n"," ['ner_anatomy_coarse', 'en', '3.0.0'],\n"," ['ner_traffic', 'de', '3.0.0'],\n"," ['ner_bionlp_biobert', 'en', '3.0.0'],\n"," ['ner_deid_sd_large', 'en', '3.0.0'],\n"," ['ner_healthcare_slim', 'de', '3.0.0'],\n"," ['ner_risk_factors_biobert', 'en', '3.0.0'],\n"," ['ner_posology_healthcare', 'en', '3.0.0'],\n"," ['ner_diseases_large', 'en', '3.0.0'],\n"," ['ner_ade_biobert', 'en', '3.0.0'],\n"," ['ner_jsl_enriched_biobert', 'en', '3.0.0'],\n"," ['ner_ade_healthcare', 'en', '3.0.0'],\n"," ['ner_deid_sd', 'en', '3.0.0'],\n"," ['ner_events_healthcare', 'en', '3.0.0'],\n"," ['ner_financial_contract', 'en', '3.0.0'],\n"," ['ner_chemprot_biobert', 'en', '3.0.0'],\n"," ['ner_ade_clinicalbert', 'en', '3.0.0'],\n"," ['ner_posology_large_biobert', 'en', '3.0.0'],\n"," ['ner_radiology_wip_clinical', 'en', '3.0.0'],\n"," ['ner_jsl_biobert', 'en', '3.0.0'],\n"," ['ner_clinical_biobert', 'en', '3.0.0'],\n"," ['ner_deid_enriched_biobert', 'en', '3.0.0'],\n"," ['ner_posology_biobert', 'en', '3.0.0'],\n"," ['ner_cellular_biobert', 'en', '3.0.0'],\n"," ['jsl_ner_wip_modifier_clinical', 'en', '3.0.0'],\n"," ['ner_measurements_clinical', 'en', '3.0.0'],\n"," ['ner_medmentions_coarse', 'en', '3.0.0'],\n"," ['ner_chemicals', 'en', '3.0.0'],\n"," ['ner_events_biobert', 'en', '3.0.0'],\n"," ['ner_bacterial_species', 'en', '3.0.0'],\n"," ['ner_diseases_biobert', 'en', '3.0.0'],\n"," ['ner_human_phenotype_gene_biobert', 'en', '3.0.0'],\n"," ['ner_deid_biobert', 'en', '3.0.0'],\n"," ['nerdl_tumour_demo', 'en', '3.0.0'],\n"," ['ner_human_phenotype_go_biobert', 'en', '3.0.0'],\n"," ['ner_anatomy_biobert', 'en', '3.0.0'],\n"," ['ner_ade_clinical', 'en', '3.0.0'],\n"," ['jsl_rd_ner_wip_greedy_clinical', 'en', '3.0.0'],\n"," ['ner_healthcare', 'en', '3.0.0'],\n"," ['ner_deid_generic_augmented', 'en', '3.0.3'],\n"," ['ner_deid_subentity_augmented', 'en', '3.0.3'],\n"," ['ner_deid_generic_glove', 'en', '3.0.4'],\n"," ['ner_deid_subentity_glove', 'en', '3.0.4'],\n"," ['ner_jsl', 'en', '3.1.0'],\n"," ['ner_genetic_variants', 'en', '3.1.0'],\n"," ['ner_deid_subentity_augmented', 'en', '3.1.0'],\n"," ['ner_deid_generic_augmented', 'en', '3.1.0'],\n"," ['lener_bert_base', 'pt', '3.1.2'],\n"," ['cantemist_scielowiki', 'es', '3.1.2'],\n"," ['jsl_ner_wip_greedy_biobert', 'en', '3.1.3'],\n"," ['jsl_rd_ner_wip_greedy_biobert', 'en', '3.1.3'],\n"," ['lener_bert_large', 'pt', '3.1.3'],\n"," ['lener_bert_base', 'pt', '3.1.3'],\n"," ['meddroprof_scielowiki', 'es', '3.1.3'],\n"," ['ner_jsl_greedy_biobert', 'en', '3.2.0'],\n"," ['ner_jsl_slim', 'en', '3.2.0'],\n"," ['ner_posology_experimental', 'en', '3.1.3'],\n"," ['ner_deid_subentity_augmented', 'en', '3.2.0'],\n"," ['ner_jsl_biobert', 'en', '3.2.0'],\n"," ['ner_chexpert', 'en', '3.3.0'],\n"," ['ner_jsl_enriched', 'en', '3.3.0'],\n"," ['ner_covid_trials', 'en', '3.3.2'],\n"," ['ner_chemd_clinical', 'en', '3.3.0'],\n"," ['roberta_ner_diag_proc', 'es', '3.3.0'],\n"," ['ner_nihss', 'en', '3.3.2'],\n"," ['ner_biomarker', 'en', '3.3.3'],\n"," ['ner_deid_subentity_augmented_i2b2', 'en', '3.3.2'],\n"," ['ner_drugprot_clinical', 'en', '3.3.3'],\n"," ['ner_abbreviation_clinical', 'en', '3.3.4'],\n"," ['ner_deid_generic', 'de', '3.3.4'],\n"," ['ner_deid_subentity', 'de', '3.3.4'],\n"," ['ner_deid_subentity_roberta', 'es', '3.3.4'],\n"," ['ner_deid_generic_roberta', 'es', '3.3.4'],\n"," ['ner_deid_generic', 'es', '3.3.4'],\n"," ['ner_deid_subentity', 'es', '3.3.4'],\n"," ['ner_supplement_clinical', 'en', '3.3.4'],\n"," ['ner_nature_nero_clinical', 'en', '3.3.4'],\n"," ['ner_deid_generic', 'fr', '3.4.1'],\n"," ['ner_deid_subentity', 'fr', '3.4.1'],\n"," ['ner_deid_generic_roberta_augmented', 'es', '3.3.4'],\n"," ['ner_deid_generic_augmented', 'es', '3.3.4'],\n"," ['ner_deid_subentity_augmented', 'es', '3.3.4'],\n"," ['ner_deid_subentity_roberta_augmented', 'es', '3.3.4'],\n"," ['ner_deid_subentity', 'it', '3.4.2'],\n"," ['ner_deid_generic', 'it', '3.4.2'],\n"," ['ner_deid_subentity', 'pt', '3.4.2'],\n"," ['ner_deid_generic', 'pt', '3.4.2'],\n"," ['ner_biomedical_bc2gm', 'en', '3.5.1'],\n"," ['ner_living_species', 'en', '3.5.3'],\n"," ['ner_living_species_roberta', 'pt', '3.5.3'],\n"," ['ner_living_species_bert', 'pt', '3.5.3'],\n"," ['ner_living_species', 'pt', '3.5.3'],\n"," ['ner_living_species', 'gl', '3.5.3'],\n"," ['ner_living_species', 'ca', '3.5.3'],\n"," ['ner_living_species_bert', 'ro', '3.5.3'],\n"," ['ner_living_species', 'fr', '3.5.3'],\n"," ['ner_living_species_bert', 'fr', '3.5.3'],\n"," ['ner_living_species', 'it', '3.5.3'],\n"," ['ner_living_species_bert', 'it', '3.5.3'],\n"," ['ner_living_species', 'es', '3.5.3'],\n"," ['ner_living_species_roberta', 'es', '3.5.3'],\n"," ['ner_living_species_bert', 'es', '3.5.3'],\n"," ['ner_living_species_biobert', 'en', '3.5.3'],\n"," ['ner_clinical_trials_abstracts', 'en', '3.5.3'],\n"," ['ner_deid_subentity_bert', 'ro', '4.0.0'],\n"," ['ner_deid_subentity', 'ro', '4.0.0'],\n"," ['ner_pathogen', 'en', '4.0.0'],\n"," ['ner_clinical_bert', 'ro', '4.0.0'],\n"," ['ner_clinical', 'ro', '4.0.0'],\n"," ['ner_deid_generic_bert', 'ro', '3.5.0'],\n"," ['ner_deid_generic', 'ro', '3.5.0'],\n"," ['ner_oncology_wip', 'en', '3.5.0'],\n"," ['ner_living_species_300', 'es', '3.5.0'],\n"," ['ner_ade_binary', 'en', '4.0.0'],\n"," ['ner_living_species_300', 'es', '4.0.0'],\n"," ['ner_clinical_bert', 'ro', '4.0.2'],\n"," ['ner_clinical_trials_abstracts', 'es', '4.0.2'],\n"," ['ner_pharmacology', 'es', '4.0.2'],\n"," ['ner_negation_uncertainty', 'es', '4.0.2'],\n"," ['disease_mentions_tweet', 'es', '4.0.2'],\n"," ['ner_deid_generic_bert', 'ro', '4.0.2'],\n"," ['ner_oncology_unspecific_posology_wip', 'en', '4.0.0'],\n"," ['ner_oncology_wip', 'en', '4.0.0'],\n"," ['ner_oncology_therapy_wip', 'en', '4.0.0'],\n"," ['ner_oncology_posology_wip', 'en', '4.0.0'],\n"," ['ner_oncology_anatomy_general_wip', 'en', '4.0.0'],\n"," ['ner_oncology_tnm_wip', 'en', '4.0.0'],\n"," ['ner_oncology_demographics_wip', 'en', '4.0.0'],\n"," ['ner_oncology_biomarker_wip', 'en', '4.0.0'],\n"," ['ner_oncology_anatomy_granular_wip', 'en', '4.0.0'],\n"," ['ner_oncology_test_wip', 'en', '4.0.0'],\n"," ['ner_oncology_diagnosis_wip', 'en', '4.0.0'],\n"," ['ner_oncology_response_to_treatment_wip', 'en', '4.0.0'],\n"," ['ner_jsl', 'en', '4.2.0'],\n"," ['ner_covid_trials', 'en', '4.2.0'],\n"," ['ner_oncology_unspecific_posology', 'en', '4.0.0'],\n"," ['ner_oncology', 'en', '4.0.0'],\n"," ['ner_oncology_tnm', 'en', '4.0.0'],\n"," ['ner_oncology_anatomy_general', 'en', '4.0.0'],\n"," ['ner_oncology_therapy', 'en', '4.0.0'],\n"," ['ner_oncology_test', 'en', '4.0.0'],\n"," ['ner_oncology_diagnosis', 'en', '4.0.0'],\n"," ['ner_oncology_demographics', 'en', '4.0.0'],\n"," ['ner_oncology_anatomy_granular', 'en', '4.0.0'],\n"," ['ner_oncology_response_to_treatment', 'en', '4.0.0'],\n"," ['ner_oncology_posology', 'en', '4.0.0'],\n"," ['ner_oncology_biomarker', 'en', '4.0.0'],\n"," ['ner_sdoh_slim_wip', 'en', '4.2.1'],\n"," ['ner_clinical_bert', 'ro', '4.2.2'],\n"," ['ner_living_species_300', 'es', '4.2.2'],\n"," ['ner_deid_generic_bert', 'ro', '4.2.2'],\n"," ['ner_oncology_biomarker', 'en', '4.2.2'],\n"," ['ner_oncology_response_to_treatment', 'en', '4.2.2'],\n"," ['ner_oncology_demographics', 'en', '4.2.2'],\n"," ['ner_oncology_therapy', 'en', '4.2.2'],\n"," ['ner_oncology', 'en', '4.2.2'],\n"," ['ner_oncology_anatomy_granular', 'en', '4.2.2'],\n"," ['ner_oncology_anatomy_general', 'en', '4.2.2'],\n"," ['ner_oncology_diagnosis', 'en', '4.2.2'],\n"," ['ner_oncology_tnm', 'en', '4.2.2'],\n"," ['ner_oncology_posology', 'en', '4.2.2'],\n"," ['ner_oncology_unspecific_posology', 'en', '4.2.2'],\n"," ['ner_oncology_test', 'en', '4.2.2'],\n"," ['meddroprof_scielowiki', 'es', '4.2.2'],\n"," ['ner_sdoh_mentions', 'en', '4.2.2'],\n"," ['ner_sdoh_mentions_test', 'en', '4.2.3'],\n"," ['ner_oncology_anatomy_general_healthcare', 'en', '4.2.4'],\n"," ['ner_oncology_biomarker_healthcare', 'en', '4.2.4'],\n"," ['ner_oncology_unspecific_posology_healthcare', 'en', '4.2.4'],\n"," ['ner_eu_clinical_case', 'en', '4.2.7'],\n"," ['ner_eu_clinical_case', 'eu', '4.2.8'],\n"," ['ner_eu_clinical_case', 'es', '4.2.8'],\n"," ['ner_eu_clinical_condition', 'es', '4.2.8'],\n"," ['ner_eu_clinical_condition', 'eu', '4.2.8'],\n"," ['ner_eu_clinical_condition', 'fr', '4.2.8'],\n"," ['ner_eu_clinical_condition', 'en', '4.2.8'],\n"," ['ner_eu_clinical_case', 'fr', '4.2.8'],\n"," ['ner_eu_clinical_condition', 'it', '4.2.8'],\n"," ['ner_sdoh_demographics_wip', 'en', '4.2.8'],\n"," ['ner_sdoh_social_environment_wip', 'en', '4.2.8'],\n"," ['ner_sdoh_income_social_status_wip', 'en', '4.2.8'],\n"," ['ner_sdoh_wip', 'en', '4.2.8'],\n"," ['ner_sdoh_substance_usage_wip', 'en', '4.3.1'],\n"," ['ner_sdoh_access_to_healthcare_wip', 'en', '4.3.1'],\n"," ['ner_sdoh_community_condition_wip', 'en', '4.3.1'],\n"," ['ner_sdoh_health_behaviours_problems_wip', 'en', '4.3.1'],\n"," ['ner_vop_slim_wip', 'en', '4.2.4'],\n"," ['ner_jsl_limited_80p_for_benchmarks', 'en', '4.3.2'],\n"," ['ner_vop_wip', 'en', '4.4.0'],\n"," ['ner_oncology_limited_80p_for_benchmarks', 'en', '4.3.2'],\n"," ['ner_vop_emb_clinical_medium_wip', 'en', '4.4.0'],\n"," ['ner_vop_emb_clinical_large_wip', 'en', '4.4.0'],\n"," ['ner_oncology_emb_clinical_large', 'en', '4.3.2'],\n"," ['ner_oncology_emb_clinical_medium', 'en', '4.3.2'],\n"," ['ner_deid_large_emb_clinical_medium', 'en', '4.3.2'],\n"," ['ner_posology_emb_clinical_medium', 'en', '4.3.2'],\n"," ['ner_deid_large_emb_clinical_large', 'en', '4.3.2'],\n"," ['ner_posology_emb_clinical_large', 'en', '4.3.2'],\n"," ['ner_jsl_emb_clinical_medium', 'en', '4.3.2'],\n"," ['ner_jsl_emb_clinical_large', 'en', '4.3.2'],\n"," ['ner_sdoh_emb_clinical_large_wip', 'en', '4.3.2'],\n"," ['ner_sdoh_emb_clinical_medium_wip', 'en', '4.3.2'],\n"," ['ner_vop_treatment_wip', 'en', '4.4.0'],\n"," ['ner_vop_problem_wip', 'en', '4.4.0'],\n"," ['ner_vop_problem_reduced_wip', 'en', '4.4.0'],\n"," ['ner_vop_temporal_wip', 'en', '4.4.0'],\n"," ['ner_vop_anatomy_wip', 'en', '4.4.0'],\n"," ['ner_vop_clinical_dept_wip', 'en', '4.4.0'],\n"," ['ner_vop_demographic_wip', 'en', '4.4.0'],\n"," ['ner_vop_test_wip', 'en', '4.4.0'],\n"," ['ner_clinical', 'de', '4.4.0'],\n"," ['ner_abbreviation_emb_clinical_large', 'en', '4.4.1'],\n"," ['ner_abbreviation_emb_clinical_medium', 'en', '4.4.1'],\n"," ['ner_anatomy_emb_clinical_medium', 'en', '4.4.1'],\n"," ['ner_anatomy_emb_clinical_large', 'en', '4.4.2'],\n"," ['ner_cellular_emb_clinical_large', 'en', '4.4.2'],\n"," ['ner_cellular_emb_clinical_medium', 'en', '4.4.2'],\n"," ['ner_ade_emb_clinical_large', 'en', '4.4.2'],\n"," ['ner_ade_emb_clinical_medium', 'en', '4.4.2'],\n"," ['ner_bacterial_species_emb_clinical_large', 'en', '4.4.2'],\n"," ['ner_bacterial_species_emb_clinical_medium', 'en', '4.4.2'],\n"," ['ner_vop_treatment_wip', 'en', '4.4.2'],\n"," ['ner_vop_clinical_dept_wip', 'en', '4.4.2'],\n"," ['ner_vop_wip_emb_clinical_large', 'en', '4.4.2'],\n"," ['ner_vop_wip_emb_clinical_medium', 'en', '4.4.2'],\n"," ['ner_vop_problem_reduced_wip', 'en', '4.4.2'],\n"," ['ner_vop_test_wip', 'en', '4.4.2'],\n"," ['ner_vop_wip', 'en', '4.4.2'],\n"," ['ner_vop_problem_wip', 'en', '4.4.2'],\n"," ['ner_vop_anatomy_wip', 'en', '4.4.2'],\n"," ['ner_vop_demographic_wip', 'en', '4.4.2'],\n"," ['ner_vop_temporal_wip', 'en', '4.4.2'],\n"," ['ner_deid_generic', 'ar', '4.4.2'],\n"," ['ner_deid_subentity', 'ar', '4.4.2'],\n"," ['ner_chemicals_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_chemicals_emb_clinical_large', 'en', '4.4.3'],\n"," ['ner_vop_problem_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_vop_treatment_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_vop_temporal_emb_clinical_large_final', 'en', '4.4.3'],\n"," ['ner_vop_temporal', 'en', '4.4.3'],\n"," ['ner_vop_clinical_dept_emb_clinical_large', 'en', '4.4.3'],\n"," ['ner_vop_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_vop_demographic_emb_clinical_large', 'en', '4.4.3'],\n"," ['ner_vop_demographic_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_vop_test_emb_clinical_large', 'en', '4.4.3'],\n"," ['ner_vop_anatomy', 'en', '4.4.3'],\n"," ['ner_vop_test_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_vop_problem_reduced', 'en', '4.4.3'],\n"," ['ner_vop_test', 'en', '4.4.3'],\n"," ['ner_vop_demographic', 'en', '4.4.3'],\n"," ['ner_vop_treatment_emb_clinical_large', 'en', '4.4.3'],\n"," ['ner_vop_emb_clinical_large', 'en', '4.4.3'],\n"," ['ner_vop_problem_emb_clinical_large', 'en', '4.4.3'],\n"," ['ner_vop_treatment', 'en', '4.4.3'],\n"," ['ner_vop_problem_reduced_emb_clinical_large', 'en', '4.4.3'],\n"," ['ner_vop', 'en', '4.4.3'],\n"," ['ner_vop_anatomy_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_vop_problem_reduced_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_vop_clinical_dept', 'en', '4.4.3'],\n"," ['ner_vop_problem', 'en', '4.4.3'],\n"," ['ner_vop_clinical_dept_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_vop_anatomy_emb_clinical_large', 'en', '4.4.3'],\n"," ['ner_vop_temporal_emb_clinical_medium', 'en', '4.4.3'],\n"," ['ner_demographic_extended_healthcare', 'en', '4.4.3'],\n"," ['ner_sdoh', 'en', '4.4.3'],\n"," ['ner_sdoh_substance_usage', 'en', '4.4.4'],\n"," ['ner_sdoh_health_behaviours_problems', 'en', '4.4.4'],\n"," ['ner_sdoh_demographics', 'en', '4.4.4'],\n"," ['ner_sdoh_income_social_status', 'en', '4.4.4'],\n"," ['ner_sdoh_social_environment', 'en', '4.4.4'],\n"," ['ner_sdoh_access_to_healthcare', 'en', '4.4.4'],\n"," ['ner_sdoh_community_condition', 'en', '4.4.4'],\n"," ['ner_clinical', 'nl', '4.4.4'],\n"," ['ner_ade_clinical_langtest', 'en', '5.0.0'],\n"," ['ner_sdoh_langtest', 'en', '5.0.0'],\n"," ['ner_jsl_langtest', 'en', '5.0.0'],\n"," ['ner_posology_langtest', 'en', '5.0.0'],\n"," ['ner_section_header_diagnosis', 'en', '5.0.0'],\n"," ['ner_clinical', 'pt', '5.0.1'],\n"," ['ner_clinical', 'es', '5.0.1'],\n"," ['ner_clinical', 'it', '5.0.1'],\n"," ['ner_clinical', 'pl', '5.0.2'],\n"," ['ner_clinical', 'tr', '5.0.2'],\n"," ['ner_clinical', 'fr', '5.0.1'],\n"," ['ner_oncology_demographics_langtest', 'en', '5.0.2'],\n"," ['ner_oncology_anatomy_general_langtest', 'en', '5.0.2'],\n"," ['ner_events_clinical_langtest', 'en', '5.0.2'],\n"," ['ner_oncology_anatomy_granular_langtest', 'en', '5.0.2'],\n"," ['ner_oncology_posology_langtest', 'en', '5.0.2'],\n"," ['ner_oncology_response_to_treatment_langtest', 'en', '5.0.2'],\n"," ['ner_deid_camelbert_generic', 'ar', '5.1.0'],\n"," ['ner_deid_generic_camelbert', 'ar', '5.1.0'],\n"," ['ner_deid_subentity_arabert', 'ar', '5.1.0'],\n"," ['ner_deid_generic_arabert', 'ar', '5.1.0'],\n"," ['ner_clinical', 'ja', '5.1.0'],\n"," ['ner_clinical', 'vi', '5.1.0'],\n"," ['ner_clinical', 'no', '5.1.0'],\n"," ['ner_clinical', 'da', '5.1.0'],\n"," ['ner_clinical', 'sv', '5.1.0'],\n"," ['ner_deid_subentity_camelbert', 'ar', '5.1.0'],\n"," ['ner_vop_anatomy_langtest', 'en', '5.1.0'],\n"," ['ner_oncology_biomarker_langtest', 'en', '5.1.0'],\n"," ['ner_vop_demographic_langtest', 'en', '5.1.0'],\n"," ['ner_vop_treatment_langtest', 'en', '5.1.0'],\n"," ['ner_vop_test_langtest', 'en', '5.1.0'],\n"," ['ner_oncology_tnm_langtest', 'en', '5.1.0'],\n"," ['ner_vop_temporal_langtest', 'en', '5.1.0'],\n"," ['ner_oncology_therapy_langtest', 'en', '5.1.0'],\n"," ['ner_oncology_test_langtest', 'en', '5.1.0'],\n"," ['ner_oncology_unspecific_posology_langtest', 'en', '5.1.0'],\n"," ['ner_eu_clinical_condition_langtest', 'en', '5.1.0'],\n"," ['ner_vop_problem_langtest', 'en', '5.1.0'],\n"," ['ner_vop_clinical_dept_langtest', 'en', '5.1.0'],\n"," ['ner_oncology_diagnosis_langtest', 'en', '5.1.0'],\n"," ['ner_vop_problem_reduced_langtest', 'en', '5.1.0'],\n"," ['ner_deid_subentity_augmented_langtest', 'en', '5.1.0'],\n"," ['ner_deid_generic_augmented_langtest', 'en', '5.1.0'],\n"," ['ner_clinical_langtest', 'en', '5.1.0'],\n"," ['ner_clinical', 'ar', '5.1.1'],\n"," ['ner_clinical', 'bg', '5.1.1'],\n"," ['ner_clinical', 'fi', '5.1.1'],\n"," ['ner_biomarker_langtest', 'en', '5.1.1'],\n"," ['ner_bionlp_langtest', 'en', '5.1.1'],\n"," ['ner_diseases_langtest', 'en', '5.1.1'],\n"," ['ner_deid_large_langtest', 'en', '5.1.1'],\n"," ['ner_cellular_langtest', 'en', '5.1.1'],\n"," ['ner_bacterial_species_langtest', 'en', '5.1.1'],\n"," ['ner_chemprot_clinical_langtest', 'en', '5.1.1'],\n"," ['ner_deid_enriched_langtest', 'en', '5.1.1'],\n"," ['ner_deid_generic_augmented_allUpperCased_langtest', 'en', '5.1.1'],\n"," ['ner_clinical_large_langtest', 'en', '5.1.1'],\n"," ['ner_living_species_langtest', 'en', '5.1.1'],\n"," ['ner_vop_langtest', 'en', '5.1.1'],\n"," ['ner_oncology_langtest', 'en', '5.1.1'],\n"," ['ner_clinical', 'he', '5.1.1'],\n"," ['ner_human_phenotype_go_clinical_langtest', 'en', '5.1.1'],\n"," ['ner_risk_factors_langtest', 'en', '5.1.1'],\n"," ['ner_human_phenotype_gene_clinical_langtest', 'en', '5.1.1'],\n"," ['ner_opioid_small_wip', 'en', '5.2.0'],\n"," ['ner_deid_name_multilingual', 'xx', '5.2.1'],\n"," ['ner_clinical_abbreviation_langtest', 'en', '5.2.1'],\n"," ['ner_deid_multilingual', 'xx', '5.2.1'],\n"," ['ner_deid_name_multilingual_clinical', 'xx', '5.2.1'],\n"," ['ner_snomed_term', 'en', '5.2.1'],\n"," ['ner_opioid', 'en', '5.2.1'],\n"," ['ner_deid_name_multilingual_clinical_langtest', 'xx', '5.3.0'],\n"," ['ner_opioid', 'en', '5.3.0'],\n"," ['ner_sdoh_core', 'en', '5.3.1']]"]},"metadata":{},"execution_count":7}],"source":["# pipelines = medical.InternalResourceDownloader.returnPrivatePipelines()\n","ner_models = medical.InternalResourceDownloader.returnPrivateModels(\"MedicalNerModel\")\n","ner_models"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":36278,"status":"ok","timestamp":1712934951533,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"SvDbCdj_g1G7","outputId":"520592bd-436d-4dce-a0ef-e4fdd71e2750"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                name sparkVersion version language  \\\n","0                  nerdl_tumour_demo            2   1.7.3       en   \n","1                 nercrf_tumour_demo            2   1.7.3       en   \n","2                  nerdl_tumour_demo          2.4   1.8.0       en   \n","3                 nercrf_tumour_demo          2.4   1.8.0       en   \n","4                 nercrf_tumour_demo          2.4   2.0.2       en   \n","...                              ...          ...     ...      ...   \n","1748                      ner_opioid          3.0   5.3.0       en   \n","1749  ner_profiling_deidentification          3.4   5.3.1       en   \n","1750  ner_profiling_deidentification          3.2   5.3.1       en   \n","1751  ner_profiling_deidentification          3.0   5.3.1       en   \n","1752                   ner_sdoh_core          3.0   5.3.1       en   \n","\n","                         date readyToUse  \n","0     2018-12-19T16:52:37.735       true  \n","1     2018-12-19T17:23:53.776       true  \n","2     2018-12-22T04:21:25.574       true  \n","3     2018-12-22T04:46:26.992       true  \n","4     2019-05-21T18:23:13.066       true  \n","...                       ...        ...  \n","1748  2024-03-27T05:10:11.243       true  \n","1749  2024-03-28T13:11:24.226       true  \n","1750  2024-03-28T12:57:39.423       true  \n","1751  2024-03-28T13:39:48.135       true  \n","1752  2024-04-08T04:12:22.813       true  \n","\n","[1753 rows x 6 columns]"],"text/html":["\n","  <div id=\"df-59548a83-b6de-4d81-bfdb-1654e6f2c3ef\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>sparkVersion</th>\n","      <th>version</th>\n","      <th>language</th>\n","      <th>date</th>\n","      <th>readyToUse</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>nerdl_tumour_demo</td>\n","      <td>2</td>\n","      <td>1.7.3</td>\n","      <td>en</td>\n","      <td>2018-12-19T16:52:37.735</td>\n","      <td>true</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","     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C","userId":"08787989274818793476"},"user_tz":-180},"id":"rGnqNHy9d6sG","outputId":"a7d01bcf-74cc-462a-fe8d-a7e4c8112cdd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                     name sparkVersion version language  \\\n","0                        ner_oncology_wip          3.0   3.5.0       en   \n","1    ner_oncology_unspecific_posology_wip          3.0   4.0.0       en   \n","2                        ner_oncology_wip          3.0   4.0.0       en   \n","3                ner_oncology_therapy_wip          3.0   4.0.0       en   \n","4               ner_oncology_posology_wip          3.0   4.0.0       en   \n","..                                    ...          ...     ...      ...   \n","135       ner_oncology_diagnosis_langtest          3.0   5.1.0       en   \n","136                 ner_oncology_langtest          3.0   5.1.1       en   \n","137                 ner_oncology_pipeline          3.0   5.2.0       en   \n","138                 ner_oncology_pipeline          3.2   5.2.0       en   \n","139                 ner_oncology_pipeline          3.4   5.2.0       en   \n","\n","                        date readyToUse  \n","0    2022-07-25T17:48:26.053       true  \n","1    2022-09-30T18:49:12.336       true  \n","2    2022-09-30T16:54:45.893       true  \n","3    2022-09-30T17:12:16.894       true  \n","4    2022-10-01T04:46:44.423       true  \n","..                       ...        ...  \n","135  2023-09-22T14:43:31.887       true  \n","136  2023-10-15T20:38:50.566       true  \n","137  2024-01-08T14:26:57.619       true  \n","138  2024-01-08T14:42:30.181       true  \n","139  2024-01-08T14:53:55.028       true  \n","\n","[140 rows x 6 columns]"],"text/html":["\n","  <div id=\"df-2f3a7853-e670-47c5-b021-74875097194d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","      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\"3.4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"version\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"4.3.2\",\n          \"3.5.0\",\n          \"5.1.1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"en\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 140,\n        \"samples\": [\n          \"2023-06-17T09:37:46.228\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"readyToUse\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"true\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":9}],"source":["# find oncology NER models\n","\n","models = compatibility.findVersion(\"ner_oncology\")\n","\n","models_df = pd.DataFrame([dict(x) for x in list(models)])\n","models_df"]},{"cell_type":"markdown","metadata":{"id":"AMU4sAJQ0Rhs"},"source":["## Clinical NER Pipeline (with pretrained models)"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":161263,"status":"ok","timestamp":1712935116373,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"DEa5SITBxmY0","outputId":"5e503266-7e0f-4108-89e4-205e9d86c5ef"},"outputs":[{"output_type":"stream","name":"stdout","text":["sentence_detector_dl_healthcare download started this may take some time.\n","Approximate size to download 367.3 KB\n","[OK!]\n","embeddings_clinical download started this may take some time.\n","Approximate size to download 1.6 GB\n","[OK!]\n","ner_clinical_large download started this may take some time.\n","[OK!]\n"]}],"source":["# Annotator that transforms a text column from dataframe into an Annotation ready for NLP\n","documentAssembler = nlp.DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","sentenceDetector = nlp.SentenceDetectorDLModel.pretrained(\"sentence_detector_dl_healthcare\",\"en\",\"clinical/models\")\\\n","    .setInputCols([\"document\"])\\\n","    .setOutputCol(\"sentence\")\n","\n","# Tokenizer splits words in a relevant format for NLP\n","tokenizer = nlp.Tokenizer()\\\n","    .setInputCols([\"sentence\"])\\\n","    .setOutputCol(\"token\")\n","\n","# Clinical word embeddings trained on PubMED dataset\n","word_embeddings = nlp.WordEmbeddingsModel.pretrained(\"embeddings_clinical\",\"en\",\"clinical/models\")\\\n","    .setInputCols([\"sentence\",\"token\"])\\\n","    .setOutputCol(\"embeddings\")\n","\n","# NER model trained on i2b2 (sampled from MIMIC) dataset\n","clinical_ner = medical.NerModel.pretrained(\"ner_clinical_large\",\"en\",\"clinical/models\")\\\n","    .setInputCols([\"sentence\",\"token\",\"embeddings\"])\\\n","    .setOutputCol(\"ner\")\\\n","    .setLabelCasing(\"upper\") #decide if we want to return the tags in upper or lower case\n","\n","ner_converter = medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\",\"token\",\"ner\"])\\\n","    .setOutputCol(\"ner_chunk\")\n","\n","nlpPipeline = nlp.Pipeline(stages=[\n","    documentAssembler,\n","    sentenceDetector,\n","    tokenizer,\n","    word_embeddings,\n","    clinical_ner,\n","    ner_converter])\n","\n","\n","empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n","\n","model = nlpPipeline.fit(empty_data)\n"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1712935116373,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"eCPdErQTt9WS","outputId":"015c8db5-73da-4576-c49e-37eea2fe0072"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[DocumentAssembler_b06127093d4f,\n"," SentenceDetectorDLModel_6bafc4746ea5,\n"," REGEX_TOKENIZER_ce8b960ac920,\n"," WORD_EMBEDDINGS_MODEL_9004b1d00302,\n"," MedicalNerModel_1a8637089929,\n"," NER_CONVERTER_9d64f35b1990]"]},"metadata":{},"execution_count":11}],"source":["model.stages"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1712935116373,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"m2hXcbMzvUzf","outputId":"dbf99a55-d73c-4c85-f1bf-a518cb550b36"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['O',\n"," 'B-TREATMENT',\n"," 'I-TREATMENT',\n"," 'B-PROBLEM',\n"," 'I-PROBLEM',\n"," 'B-TEST',\n"," 'I-TEST']"]},"metadata":{},"execution_count":12}],"source":["clinical_ner.getClasses()"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712935116373,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"rPO8wgprvai_","outputId":"133c2699-291d-46cf-e91d-792b855ceb01"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{Param(parent='MedicalNerModel_1a8637089929', name='doExceptionHandling', doc='If true, tries to compute batch-wise as usual.\\n    If within a batch an exception occurs, the batch will be processed row-wise and for any exception occurring during row-processing a Error Annotation is emitted.\\n    This means only the bad rows are lost instead of entire batch.\\n    This comes with a performance penalty.'): False,\n"," Param(parent='MedicalNerModel_1a8637089929', name='includeAllConfidenceScores', doc='whether to include all confidence scores in annotation metadata or just the score of the predicted tag'): False,\n"," Param(parent='MedicalNerModel_1a8637089929', name='inferenceBatchSize', doc='number of sentences to process in a single batch during inference'): 1,\n"," Param(parent='MedicalNerModel_1a8637089929', name='labelCasing', doc='Setting all labels of the NER models upper/lower case. values upper|lower'): 'upper',\n"," Param(parent='MedicalNerModel_1a8637089929', name='sentenceTokenIndex', doc='Whether to include the token index for each sentence in annotation metadata.'): False,\n"," Param(parent='MedicalNerModel_1a8637089929', name='lazyAnnotator', doc='Whether this AnnotatorModel acts as lazy in RecursivePipelines'): False,\n"," Param(parent='MedicalNerModel_1a8637089929', name='includeConfidence', doc='whether to include confidence scores in annotation metadata'): True,\n"," Param(parent='MedicalNerModel_1a8637089929', name='batchSize', doc='Size of every batch'): 64,\n"," Param(parent='MedicalNerModel_1a8637089929', name='classes', doc='get the tags used to trained this MedicalNerModel'): ['O',\n","  'B-TREATMENT',\n","  'I-TREATMENT',\n","  'B-PROBLEM',\n","  'I-PROBLEM',\n","  'B-TEST',\n","  'I-TEST'],\n"," Param(parent='MedicalNerModel_1a8637089929', name='inputCols', doc='previous annotations columns, if renamed'): ['sentence',\n","  'token',\n","  'embeddings'],\n"," Param(parent='MedicalNerModel_1a8637089929', name='outputCol', doc='output annotation column. can be left default.'): 'ner',\n"," Param(parent='MedicalNerModel_1a8637089929', name='storageRef', doc='unique reference name for identification'): 'clinical'}"]},"metadata":{},"execution_count":13}],"source":["clinical_ner.extractParamMap()"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712935116374,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"gGjoRR5Bvj-U","outputId":"ebf4f172-4b6c-4c5d-b3a2-abe1463a45f7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'clinical'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":14}],"source":["clinical_ner.getStorageRef()"]},{"cell_type":"code","execution_count":15,"metadata":{"id":"v1lmxbPt1dZW","executionInfo":{"status":"ok","timestamp":1712935116978,"user_tz":-180,"elapsed":610,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["# Downloading sample datasets.\n","! wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Certification_Trainings/Healthcare/data/mt_samples_10.csv"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"f1S9J6CASrSE","executionInfo":{"status":"ok","timestamp":1712935117479,"user_tz":-180,"elapsed":502,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["mt_samples_df = spark.read.csv(\"mt_samples_10.csv\", header=True, multiLine=True)"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1712935117479,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"Kt_9Qfmw30rA","outputId":"132c14e4-ba72-438c-e532-3640ae45c691"},"outputs":[{"output_type":"stream","name":"stdout","text":["root\n"," |-- index: string (nullable = true)\n"," |-- text: string (nullable = true)\n","\n"]}],"source":["mt_samples_df.printSchema()"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1004,"status":"ok","timestamp":1712935118481,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"te55tz4dwAB5","outputId":"4f4841e6-f149-492e-b049-1770f765c99b"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----+--------------------+\n","|index|                text|\n","+-----+--------------------+\n","|    0|Sample Type / Med...|\n","|    1|Sample Type / Med...|\n","|    2|Sample Type / Med...|\n","|    3|Sample Type / Med...|\n","|    4|Sample Type / Med...|\n","|    5|Sample Type / Med...|\n","|    6|Sample Type / Med...|\n","|    7|Sample Type / Med...|\n","|    8|Sample Type / Med...|\n","|    9|Sample Type / Med...|\n","+-----+--------------------+\n","\n"]}],"source":["mt_samples_df.show()"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1712935118481,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"m5soib4Tw4PK","outputId":"5bb282c8-354f-46b1-f028-f7b020e2118c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Sample Type / Medical Specialty:\n","Hematology - Oncology\n","Sample Name:\n","Discharge Summary - Mesothelioma - 1\n","Description:\n","Mesothelioma, pleural effusion, atrial fibrillation, anemia, ascites, esophageal reflux, and history of deep venous thrombosis.\n","(Medical Transcription Sample Report)\n","PRINCIPAL DIAGNOSIS:\n","Mesothelioma.\n","SECONDARY DIAGNOSES:\n","Pleural effusion, atrial fibrillation, anemia, ascites, esophageal reflux, and history of deep venous thrombosis.\n","PROCEDURES\n","1. On August 24, 2007, decortication of the lung with pleural biopsy and transpleural fluoroscopy.\n","2. On August 20, 2007, thoracentesis.\n","3. On August 31, 2007, Port-A-Cath placement.\n","HISTORY AND PHYSICAL:\n","The patient is a 41-year-old Vietnamese female with a nonproductive cough that started last week. She has had right-sided chest pain radiating to her back with fever starting yesterday. She has a history of pericarditis and pericardectomy in May 2006 and developed cough with right-sided chest pain, and went to an urgent care center. Chest x-ray revealed right-sided pleural effusion.\n","PAST MEDICAL HISTORY\n","1. Pericardectomy.\n","2. Pericarditis.\n","2. Atrial fibrillation.\n","4. RNCA with intracranial thrombolytic treatment.\n","5 PTA of MCA.\n","6. Mesenteric venous thrombosis.\n","7. Pericardial window.\n","8. Cholecystectomy.\n","9. Left thoracentesis.\n","FAMILY HISTORY:\n","No family history of coronary artery disease, CVA, diabetes, CHF or MI. The patient has one family member, a sister, with history of cancer.\n","SOCIAL HISTORY:\n","She is married. Employed with the US Post Office. She is a mother of three. Denies tobacco, alcohol or illicit drug use.\n","MEDICATIONS\n","1. Coumadin 1 mg daily. Last INR was on Tuesday, August 14, 2007, and her INR was 2.3.\n","2. Amiodarone 100 mg p.o. daily.\n","REVIEW OF SYSTEMS:\n","Complete review of systems negative except as in pulmonary as noted above. The patient also reports occasional numbness and tingling of her left arm.\n","PHYSICAL EXAMINATION\n","VITAL SIGNS: Blood pressure 123/95, heart rate 83, respirations 20, temperature 97, and oxygen saturation 97%.\n","GENERAL: Positive nonproductive cough and pain with coughing.\n","HEENT: Pupils are equal and reactive to light and accommodation. Tympanic membranes are clear.\n","NECK: Supple. No lymphadenopathy. No masses.\n","RESPIRATORY: Pleural friction rub is noted.\n","GI: Soft, nondistended, and nontender. Positive bowel sounds. No organomegaly.\n","EXTREMITIES: No edema, no clubbing, no cyanosis, no tenderness. Full range of motion. Normal pulses in all extremities.\n","SKIN: No breakdown or lesions. No ulcers.\n","NEUROLOGIC: Grossly intact. No focal deficits. Awake, alert, and oriented to person, place, and time.\n","LABORATORY DATA:\n","Labs are pending.\n","HOSPITAL COURSE:\n","The patient was admitted for a right-sided pleural effusion for thoracentesis on Monday by Dr. X. Her Coumadin was placed on hold. A repeat echocardiogram was checked. She was started on prophylaxis for DVT with Lovenox 40 mg subcutaneously. Her history dated back to March 2005 when she first sought medical attention for evidence of pericarditis, which was treated with pericardial window in an outside hospital, at that time she was also found to have mesenteric pain and thrombosis, is now anticoagulated. Her pericardial fluid was accumulated and she was seen by Dr. Y. At that time, she was recommended for pericardectomy, which was performed by Dr. Z. Review of her CT scan from March 2006 prior to her pericardectomy, already shows bilateral plural effusions. The patient improved clinically after the pericardectomy with resolution of her symptoms. Recently, she was readmitted to the hospital with chest pain and found to have bilateral pleural effusion, the right greater than the left. CT of the chest also revealed a large mediastinal lymph node. We reviewed the pathology obtained from the pericardectomy in March 2006, which was diagnostic of mesothelioma. At this time, chest tube placement for drainage of the fluid occurred and thoracoscopy with fluid biopsies, which were performed, which revealed epithelioid malignant mesothelioma. The patient was then stained with a PET CT, which showed extensive uptake in the chest, bilateral pleural pericardial effusions, and lymphadenopathy. She also had acidic fluid, pectoral and intramammary lymph nodes and uptake in L4 with SUV of 4. This was consistent with stage III disease. Her repeat echocardiogram showed an ejection fraction of 45% to 49%. She was transferred to Oncology service and started on chemotherapy on September 1, 2007 with cisplatin 75 mg/centimeter squared equaling 109 mg IV piggyback over 2 hours on September 1, 2007, Alimta 500 mg/ centimeter squared equaling 730 mg IV piggyback over 10 minutes. This was all initiated after a Port-A-Cath was placed. The chemotherapy was well tolerated and the patient was discharged the following day after discontinuing IV fluid and IV. Her Port-A-Cath was packed with heparin according to protocol.\n","DISCHARGE MEDICATIONS:\n","Zofran, Phenergan, Coumadin, and Lovenox, and Vicodin\n","DISCHARGE INSTRUCTIONS:\n","She was instructed to followup with Dr. XYZ in the office to check her INR on Tuesday. She was instructed to call if she had any other questions or concerns in the interim.\n","Keywords:\n","hematology - oncology, mesothelioma, pleural effusion, atrial fibrillation, anemia, ascites, esophageal reflux, deep venous thrombosis, port-a-cath placement, port a cath, iv piggyback, venous thrombosis, atrial, thrombosis, pericardial, lymphadenopathy, fluid, pericardectomy, chest, pleural,\n"]}],"source":["print(mt_samples_df.limit(1).collect()[0]['text'])"]},{"cell_type":"code","execution_count":20,"metadata":{"id":"7HdysGLU-xfP","executionInfo":{"status":"ok","timestamp":1712935119113,"user_tz":-180,"elapsed":633,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["result = model.transform(mt_samples_df).cache()"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12107,"status":"ok","timestamp":1712935131218,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"DHC3kvfIF2tm","outputId":"c45e2475-9e5a-41ad-b22b-7ebb4f4df671"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|index|                text|            document|            sentence|               token|          embeddings|                 ner|           ner_chunk|\n","+-----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|    0|Sample Type / Med...|[{document, 0, 54...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 118, 129...|\n","|    1|Sample Type / Med...|[{document, 0, 32...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 68, 92, ...|\n","|    2|Sample Type / 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53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 155, 189...|\n","|    9|Sample Type / Med...|[{document, 0, 19...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 68, 95, ...|\n","+-----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","\n"]}],"source":["result.show()"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1712935131219,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"OGBHfjMVUoCu","outputId":"a20c1106-e406-453e-cb35-cd69e5e8e945"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+\n","|                                                                                              result|                                                                                              result|\n","+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Discharge, Sum...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-PROBLEM, O, B-PROBLEM, I-PROBLEM, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, BCCa, Excision...|[O, O, O, O, O, O, O, O, O, O, O, O, B-TREATMENT, I-TREATMENT, I-TREATMENT, I-TREATMENT, I-TREATM...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Anemia, -, Con...|[O, O, O, O, O, O, O, O, O, O, O, O, B-PROBLEM, O, O, O, O, B-PROBLEM, I-PROBLEM, O, O, B-PROBLEM...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Intensity-Modu...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-TREATMENT, I-TREATMENT, I-TREATMENT, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Neck, Dissecti...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-PROBLEM, I-PROBLEM, I-PROBLEM, O, B-PROBLEM, I...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, HDR, Brachythe...|[O, O, O, O, O, O, O, O, O, O, O, O, B-TREATMENT, I-TREATMENT, O, O, B-TREATMENT, I-TREATMENT, O,...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Biopsy, -, Cer...|[O, O, O, O, O, O, O, O, O, O, O, O, B-TEST, O, O, O, O, O, O, B-TEST, I-TEST, I-TEST, I-TEST, I-...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Metastatic, Ov...|[O, O, O, O, O, O, O, O, O, O, O, O, B-PROBLEM, I-PROBLEM, I-PROBLEM, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Breast, Radiat...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-PROBLEM, I-PROBLEM, I-...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Lymph, Node, E...|[O, O, O, O, O, O, O, O, O, O, O, O, B-TEST, I-TEST, I-TEST, I-TEST, O, O, B-TEST, I-TEST, I-TEST...|\n","+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+\n","\n","CPU times: user 7.37 ms, sys: 343 µs, total: 7.71 ms\n","Wall time: 229 ms\n"]}],"source":["%%time\n","\n","result.select('token.result','ner.result').show(truncate=100)"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712935131219,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"5uBnSe0--_Tn","outputId":"40eb1bf8-b97e-455d-f2c4-d7ddf7275966"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-------------+---------+----------+\n","|        token|ner_label|confidence|\n","+-------------+---------+----------+\n","|       Sample|        O|    0.9996|\n","|         Type|        O|    0.9926|\n","|            /|        O|    0.9755|\n","|      Medical|        O|    0.9824|\n","|    Specialty|        O|    0.9993|\n","|            :|        O|       1.0|\n","|   Hematology|        O|    0.9971|\n","|            -|        O|    0.9908|\n","|     Oncology|        O|    0.9796|\n","|       Sample|        O|    0.9999|\n","|         Name|        O|    0.9994|\n","|            :|        O|       1.0|\n","|    Discharge|        O|    0.9998|\n","|      Summary|        O|    0.9926|\n","|            -|        O|    0.9959|\n","| Mesothelioma|        O|    0.8117|\n","|            -|        O|    0.9934|\n","|            1|        O|    0.7394|\n","|  Description|        O|    0.9995|\n","|            :|        O|    0.9999|\n","| Mesothelioma|B-PROBLEM|    0.9991|\n","|            ,|        O|    0.9999|\n","|      pleural|B-PROBLEM|    0.9911|\n","|     effusion|I-PROBLEM|    0.9982|\n","|            ,|        O|    0.9999|\n","|       atrial|B-PROBLEM|    0.9976|\n","| fibrillation|I-PROBLEM|    0.9982|\n","|            ,|        O|    0.9999|\n","|       anemia|B-PROBLEM|     0.999|\n","|            ,|        O|    0.9999|\n","|      ascites|B-PROBLEM|    0.9996|\n","|            ,|        O|       1.0|\n","|   esophageal|B-PROBLEM|    0.9982|\n","|       reflux|I-PROBLEM|    0.9976|\n","|            ,|        O|       1.0|\n","|          and|        O|    0.9996|\n","|      history|        O|    0.9892|\n","|           of|        O|    0.9769|\n","|         deep|B-PROBLEM|    0.9421|\n","|       venous|I-PROBLEM|    0.9624|\n","|   thrombosis|I-PROBLEM|    0.9983|\n","|            .|        O|       1.0|\n","|            (|        O|    0.9999|\n","|      Medical|        O|     0.986|\n","|Transcription|        O|    0.9214|\n","|       Sample|        O|     0.993|\n","|       Report|        O|    0.9536|\n","|            )|        O|    0.9802|\n","|    PRINCIPAL|        O|    0.9981|\n","|    DIAGNOSIS|        O|    0.4728|\n","+-------------+---------+----------+\n","only showing top 50 rows\n","\n"]}],"source":["result_df = result.select(F.explode(F.arrays_zip(result.token.result,result.ner.result, result.ner.metadata)).alias(\"cols\"))\\\n","                  .select(F.expr(\"cols['0']\").alias(\"token\"),\n","                          F.expr(\"cols['1']\").alias(\"ner_label\"),\n","                          F.expr(\"cols['2']['confidence']\").alias(\"confidence\"))\n","\n","result_df.show(50, truncate=100)"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1079,"status":"ok","timestamp":1712935132294,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"Y4eM-zZcErru","outputId":"4c2f4004-bac7-4ff0-cfec-8b278584050a"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------+-----+\n","|ner_label  |count|\n","+-----------+-----+\n","|O          |4721 |\n","|I-PROBLEM  |526  |\n","|B-PROBLEM  |399  |\n","|I-TREATMENT|326  |\n","|B-TREATMENT|265  |\n","|I-TEST     |173  |\n","|B-TEST     |142  |\n","+-----------+-----+\n","\n"]}],"source":["result_df.select(\"token\", \"ner_label\")\\\n","         .groupBy('ner_label').count()\\\n","         .orderBy('count', ascending=False)\\\n","         .show(truncate=False)"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1064,"status":"ok","timestamp":1712935133356,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"uzkwbP21ydAK","outputId":"be50cc2f-5488-4397-e95a-c9e6aaa3bbd6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Row(ner_chunk=[Row(annotatorType='chunk', begin=118, end=129, result='Mesothelioma', metadata={'sentence': '2', 'chunk': '0', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'confidence': '0.9991'}, embeddings=[]), Row(annotatorType='chunk', begin=132, end=147, result='pleural effusion', 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{'annotatorType': 'chunk',\n","  'begin': 5391,\n","  'end': 5400,\n","  'result': 'thrombosis',\n","  'metadata': {'sentence': '79',\n","   'chunk': '143',\n","   'ner_source': 'ner_chunk',\n","   'entity': 'PROBLEM',\n","   'confidence': '0.9317'}},\n"," {'annotatorType': 'chunk',\n","  'begin': 5403,\n","  'end': 5413,\n","  'result': 'pericardial',\n","  'metadata': {'sentence': '79',\n","   'chunk': '144',\n","   'ner_source': 'ner_chunk',\n","   'entity': 'TREATMENT',\n","   'confidence': '0.7272'}},\n"," {'annotatorType': 'chunk',\n","  'begin': 5416,\n","  'end': 5430,\n","  'result': 'lymphadenopathy',\n","  'metadata': {'sentence': '79',\n","   'chunk': '145',\n","   'ner_source': 'ner_chunk',\n","   'entity': 'PROBLEM',\n","   'confidence': '0.8981'}},\n"," {'annotatorType': 'chunk',\n","  'begin': 5433,\n","  'end': 5437,\n","  'result': 'fluid',\n","  'metadata': {'sentence': '79',\n","   'chunk': '146',\n","   'ner_source': 'ner_chunk',\n","   'entity': 'PROBLEM',\n","   'confidence': '0.6176'}},\n"," {'annotatorType': 'chunk',\n","  'begin': 5440,\n","  'end': 5453,\n","  'result': 'pericardectomy',\n","  'metadata': {'sentence': '79',\n","   'chunk': '147',\n","   'ner_source': 'ner_chunk',\n","   'entity': 'TREATMENT',\n","   'confidence': '0.9822'}},\n"," {'annotatorType': 'chunk',\n","  'begin': 5456,\n","  'end': 5469,\n","  'result': 'chest, pleural',\n","  'metadata': {'sentence': '79',\n","   'chunk': '148',\n","   'ner_source': 'ner_chunk',\n","   'entity': 'PROBLEM',\n","   'confidence': '0.7570999'}}]"]},"metadata":{},"execution_count":26}],"source":["row_list = [{'annotatorType': row.annotatorType,\n","             'begin': row.begin,\n","             'end': row.end,\n","             'result': row.result,\n","             'metadata': row.metadata}\n","            for row in result.select('ner_chunk').take(1)[0][0]\n","           ]\n","\n","row_list"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26,"status":"ok","timestamp":1712935133356,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"iICfEeNoAklD","outputId":"d20576ad-e225-4243-828d-94ad4996bd68"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-------------------------+---------+----------+\n","|chunk                    |ner_label|confidence|\n","+-------------------------+---------+----------+\n","|Mesothelioma             |PROBLEM  |0.9991    |\n","|pleural effusion         |PROBLEM  |0.99465   |\n","|atrial fibrillation      |PROBLEM  |0.9979    |\n","|anemia                   |PROBLEM  |0.999     |\n","|ascites                  |PROBLEM  |0.9996    |\n","|esophageal reflux        |PROBLEM  |0.9979    |\n","|deep venous thrombosis   |PROBLEM  |0.96760005|\n","|Mesothelioma             |PROBLEM  |0.987     |\n","|Pleural effusion         |PROBLEM  |0.97895   |\n","|atrial fibrillation      |PROBLEM  |0.99475   |\n","|anemia                   |PROBLEM  |0.9984    |\n","|ascites                  |PROBLEM  |0.9994    |\n","|esophageal reflux        |PROBLEM  |0.99724996|\n","|deep venous thrombosis   |PROBLEM  |0.9550001 |\n","|decortication of the lung|TREATMENT|0.756325  |\n","|pleural biopsy           |TEST     |0.87715   |\n","|transpleural fluoroscopy |TEST     |0.9527    |\n","|thoracentesis            |TREATMENT|0.9075    |\n","|Port-A-Cath placement    |TREATMENT|0.98075   |\n","|a nonproductive cough    |PROBLEM  |0.96173334|\n","+-------------------------+---------+----------+\n","only showing top 20 rows\n","\n"]}],"source":["result.select(F.explode(F.arrays_zip(result.ner_chunk.result,\n","                                     result.ner_chunk.metadata)).alias(\"cols\")) \\\n","      .select(F.expr(\"cols['0']\").alias(\"chunk\"),\n","              F.expr(\"cols['1']['entity']\").alias(\"ner_label\"),\n","              F.expr(\"cols['1']['confidence']\").alias(\"confidence\")).show(truncate=False)"]},{"cell_type":"markdown","source":["For the alternative explosion method, please visit [Flattener](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators#flattener)"],"metadata":{"id":"JPYUKF47ROgI"}},{"cell_type":"markdown","metadata":{"id":"loYjPzwKx5zW"},"source":["### LightPipelines\n","\n","LightPipelines are Spark NLP specific Pipelines, equivalent to Spark ML Pipeline, but meant to deal with smaller amounts of data. They’re useful working with small datasets, debugging results, or when running either training or prediction from an API that serves one-off requests.\n","\n","**It is nearly 10x faster than using Spark ML Pipeline**\n","\n","Syntax: `LightPipeline(someTrainedPipeline).annotate(someStringOrArray)`\n","\n","For more details, please check: https://medium.com/spark-nlp/spark-nlp-101-lightpipeline-a544e93f20f1"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":783},"executionInfo":{"elapsed":1137,"status":"ok","timestamp":1712935134490,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"Z5q0BojoAsu3","outputId":"b674871f-23b1-4042-a1ff-66d8537a51e4"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus (T2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, and associated with an acute hepatitis, presented with a one-week history of polyuria, poor appetite, and vomiting.\n","She was on metformin, glipizide, and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG. She had been on dapagliflozin for six months at the time of presentation.\n","Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness, guarding, or rigidity. Pertinent laboratory findings on admission were: serum glucose 111 mg/dl,  creatinine 0.4 mg/dL, triglycerides 508 mg/dL, total cholesterol 122 mg/dL, and venous pH 7.27.\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["                                   chunks  begin  end sentence_id   entities\n","0           gestational diabetes mellitus     40   68           0    PROBLEM\n","1   subsequent type two diabetes mellitus    118  154           0    PROBLEM\n","2                                    T2DM    157  160           0    PROBLEM\n","3                HTG-induced pancreatitis    185  208           0    PROBLEM\n","4                      an acute hepatitis    265  282           0    PROBLEM\n","5                                polyuria    322  329           0    PROBLEM\n","6                           poor appetite    332  344           0    PROBLEM\n","7                                vomiting    351  358           0    PROBLEM\n","8                               metformin    372  380           1  TREATMENT\n","9                               glipizide    383  391           1  TREATMENT\n","10                          dapagliflozin    398  410           1  TREATMENT\n","11                                   T2DM    416  419           1    PROBLEM\n","12                           atorvastatin    425  436           1  TREATMENT\n","13                            gemfibrozil    442  452           1  TREATMENT\n","14                                    HTG    458  460           1    PROBLEM\n","15                          dapagliflozin    479  491           2  TREATMENT\n","16                   Physical examination    537  556           3       TEST\n","17                        dry oral mucosa    594  608           3    PROBLEM\n","18              her abdominal examination    628  652           3       TEST\n","19                             tenderness    673  682           3    PROBLEM"],"text/html":["\n","  <div 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<th>13</th>\n","      <td>gemfibrozil</td>\n","      <td>442</td>\n","      <td>452</td>\n","      <td>1</td>\n","      <td>TREATMENT</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>HTG</td>\n","      <td>458</td>\n","      <td>460</td>\n","      <td>1</td>\n","      <td>PROBLEM</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>dapagliflozin</td>\n","      <td>479</td>\n","      <td>491</td>\n","      <td>2</td>\n","      <td>TREATMENT</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Physical examination</td>\n","      <td>537</td>\n","      <td>556</td>\n","      <td>3</td>\n","      <td>TEST</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>dry oral mucosa</td>\n","      <td>594</td>\n","      <td>608</td>\n","      <td>3</td>\n","      <td>PROBLEM</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>her abdominal examination</td>\n","      <td>628</td>\n","      <td>652</td>\n","      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She had been on dapagliflozin for six months at the time of presentation.\n","Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness, guarding, or rigidity. Pertinent laboratory findings on admission were: serum glucose 111 mg/dl,  creatinine 0.4 mg/dL, triglycerides 508 mg/dL, total cholesterol 122 mg/dL, and venous pH 7.27.\n","'''\n","\n","print (text)\n","\n","light_model = nlp.LightPipeline(model)\n","\n","light_result = light_model.fullAnnotate(text)\n","\n","\n","chunks = []\n","entities = []\n","sentence= []\n","begin = []\n","end = []\n","\n","for n in light_result[0]['ner_chunk']:\n","\n","    begin.append(n.begin)\n","    end.append(n.end)\n","    chunks.append(n.result)\n","    entities.append(n.metadata['entity'])\n","    sentence.append(n.metadata['sentence'])\n","\n","\n","\n","df_clinical = pd.DataFrame({'chunks':chunks,\n","                            'begin': begin,\n","                            'end':end,\n","                            'sentence_id':sentence,\n","                            'entities':entities})\n","\n","df_clinical.head(20)"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8803,"status":"ok","timestamp":1712935143289,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"-r7yfZitmbPT","outputId":"e6d3ac0b-3d19-467f-aa5c-0105a1c7f929"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'document': [Annotation(document, 0, 878, \n","  A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus (T2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, and associated with an acute hepatitis, presented with a one-week history of polyuria, poor appetite, and vomiting.\n","  She was on metformin, glipizide, and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG. She had been on dapagliflozin for six months at the time of presentation.\n","  Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness, guarding, or rigidity. Pertinent laboratory findings on admission were: serum glucose 111 mg/dl,  creatinine 0.4 mg/dL, triglycerides 508 mg/dL, total cholesterol 122 mg/dL, and venous pH 7.27.\n","  , {}, [])],\n"," 'ner_chunk': [Annotation(chunk, 40, 68, gestational diabetes mellitus, {'chunk': '0', 'confidence': '0.91976666', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '0'}, []),\n","  Annotation(chunk, 118, 154, subsequent type two diabetes mellitus, {'chunk': '1', 'confidence': '0.75924003', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '0'}, []),\n","  Annotation(chunk, 157, 160, T2DM, {'chunk': '2', 'confidence': '0.9917', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '0'}, []),\n","  Annotation(chunk, 185, 208, HTG-induced pancreatitis, {'chunk': '3', 'confidence': '0.97535', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '0'}, []),\n","  Annotation(chunk, 265, 282, an acute hepatitis, {'chunk': '4', 'confidence': '0.9440667', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '0'}, []),\n","  Annotation(chunk, 322, 329, polyuria, {'chunk': '5', 'confidence': '0.9728', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '0'}, []),\n","  Annotation(chunk, 332, 344, poor appetite, {'chunk': '6', 'confidence': '0.9934', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '0'}, []),\n","  Annotation(chunk, 351, 358, vomiting, {'chunk': '7', 'confidence': '0.9854', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '0'}, []),\n","  Annotation(chunk, 372, 380, metformin, {'chunk': '8', 'confidence': '0.9998', 'ner_source': 'ner_chunk', 'entity': 'TREATMENT', 'sentence': '1'}, []),\n","  Annotation(chunk, 383, 391, glipizide, {'chunk': '9', 'confidence': '0.9999', 'ner_source': 'ner_chunk', 'entity': 'TREATMENT', 'sentence': '1'}, []),\n","  Annotation(chunk, 398, 410, dapagliflozin, {'chunk': '10', 'confidence': '1.0', 'ner_source': 'ner_chunk', 'entity': 'TREATMENT', 'sentence': '1'}, []),\n","  Annotation(chunk, 416, 419, T2DM, {'chunk': '11', 'confidence': '0.9995', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '1'}, []),\n","  Annotation(chunk, 425, 436, atorvastatin, {'chunk': '12', 'confidence': '0.9999', 'ner_source': 'ner_chunk', 'entity': 'TREATMENT', 'sentence': '1'}, []),\n","  Annotation(chunk, 442, 452, gemfibrozil, {'chunk': '13', 'confidence': '1.0', 'ner_source': 'ner_chunk', 'entity': 'TREATMENT', 'sentence': '1'}, []),\n","  Annotation(chunk, 458, 460, HTG, {'chunk': '14', 'confidence': '0.9994', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '1'}, []),\n","  Annotation(chunk, 479, 491, dapagliflozin, {'chunk': '15', 'confidence': '0.9999', 'ner_source': 'ner_chunk', 'entity': 'TREATMENT', 'sentence': '2'}, []),\n","  Annotation(chunk, 537, 556, Physical examination, {'chunk': '16', 'confidence': '0.9448', 'ner_source': 'ner_chunk', 'entity': 'TEST', 'sentence': '3'}, []),\n","  Annotation(chunk, 594, 608, dry oral mucosa, {'chunk': '17', 'confidence': '0.7242333', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '3'}, []),\n","  Annotation(chunk, 628, 652, her abdominal examination, {'chunk': '18', 'confidence': '0.8749666', 'ner_source': 'ner_chunk', 'entity': 'TEST', 'sentence': '3'}, []),\n","  Annotation(chunk, 673, 682, tenderness, {'chunk': '19', 'confidence': '0.9996', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '3'}, []),\n","  Annotation(chunk, 685, 692, guarding, {'chunk': '20', 'confidence': '0.9989', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '3'}, []),\n","  Annotation(chunk, 698, 705, rigidity, {'chunk': '21', 'confidence': '0.9986', 'ner_source': 'ner_chunk', 'entity': 'PROBLEM', 'sentence': '3'}, []),\n","  Annotation(chunk, 757, 769, serum glucose, {'chunk': '22', 'confidence': '0.969', 'ner_source': 'ner_chunk', 'entity': 'TEST', 'sentence': '4'}, []),\n","  Annotation(chunk, 783, 792, creatinine, {'chunk': '23', 'confidence': '0.9994', 'ner_source': 'ner_chunk', 'entity': 'TEST', 'sentence': '4'}, []),\n","  Annotation(chunk, 805, 817, triglycerides, {'chunk': '24', 'confidence': '0.9999', 'ner_source': 'ner_chunk', 'entity': 'TEST', 'sentence': '4'}, []),\n","  Annotation(chunk, 830, 846, total cholesterol, {'chunk': '25', 'confidence': '0.9827', 'ner_source': 'ner_chunk', 'entity': 'TEST', 'sentence': '4'}, []),\n","  Annotation(chunk, 863, 871, venous pH, {'chunk': '26', 'confidence': '0.97185004', 'ner_source': 'ner_chunk', 'entity': 'TEST', 'sentence': '4'}, [])],\n"," 'token': [Annotation(token, 1, 1, A, {'sentence': '0'}, []),\n","  Annotation(token, 3, 13, 28-year-old, {'sentence': '0'}, []),\n","  Annotation(token, 15, 20, female, {'sentence': '0'}, []),\n","  Annotation(token, 22, 25, with, {'sentence': '0'}, []),\n","  Annotation(token, 27, 27, a, {'sentence': '0'}, []),\n","  Annotation(token, 29, 35, history, {'sentence': '0'}, []),\n","  Annotation(token, 37, 38, of, {'sentence': '0'}, []),\n","  Annotation(token, 40, 50, gestational, {'sentence': '0'}, []),\n","  Annotation(token, 52, 59, diabetes, {'sentence': '0'}, []),\n","  Annotation(token, 61, 68, mellitus, {'sentence': '0'}, []),\n","  Annotation(token, 70, 78, diagnosed, {'sentence': '0'}, []),\n","  Annotation(token, 80, 84, eight, {'sentence': '0'}, []),\n","  Annotation(token, 86, 90, years, {'sentence': '0'}, []),\n","  Annotation(token, 92, 96, prior, {'sentence': '0'}, []),\n","  Annotation(token, 98, 99, to, {'sentence': '0'}, []),\n","  Annotation(token, 101, 112, presentation, {'sentence': '0'}, []),\n","  Annotation(token, 114, 116, and, {'sentence': '0'}, []),\n","  Annotation(token, 118, 127, subsequent, {'sentence': '0'}, []),\n","  Annotation(token, 129, 132, type, {'sentence': '0'}, []),\n","  Annotation(token, 134, 136, two, {'sentence': '0'}, []),\n","  Annotation(token, 138, 145, diabetes, {'sentence': '0'}, []),\n","  Annotation(token, 147, 154, mellitus, {'sentence': '0'}, []),\n","  Annotation(token, 156, 156, (, {'sentence': '0'}, []),\n","  Annotation(token, 157, 160, T2DM, {'sentence': '0'}, []),\n","  Annotation(token, 161, 162, ),, {'sentence': '0'}, []),\n","  Annotation(token, 164, 166, one, {'sentence': '0'}, []),\n","  Annotation(token, 168, 172, prior, {'sentence': '0'}, []),\n","  Annotation(token, 174, 180, episode, {'sentence': '0'}, []),\n","  Annotation(token, 182, 183, of, {'sentence': '0'}, []),\n","  Annotation(token, 185, 195, HTG-induced, {'sentence': '0'}, []),\n","  Annotation(token, 197, 208, pancreatitis, {'sentence': '0'}, []),\n","  Annotation(token, 210, 214, three, {'sentence': '0'}, []),\n","  Annotation(token, 216, 220, years, {'sentence': '0'}, []),\n","  Annotation(token, 222, 226, prior, {'sentence': '0'}, []),\n","  Annotation(token, 228, 229, to, {'sentence': '0'}, []),\n","  Annotation(token, 231, 242, presentation, {'sentence': '0'}, []),\n","  Annotation(token, 243, 243, ,, {'sentence': '0'}, []),\n","  Annotation(token, 245, 247, and, {'sentence': '0'}, []),\n","  Annotation(token, 249, 258, associated, {'sentence': '0'}, []),\n","  Annotation(token, 260, 263, with, {'sentence': '0'}, []),\n","  Annotation(token, 265, 266, an, {'sentence': '0'}, []),\n","  Annotation(token, 268, 272, acute, {'sentence': '0'}, []),\n","  Annotation(token, 274, 282, hepatitis, {'sentence': '0'}, []),\n","  Annotation(token, 283, 283, ,, {'sentence': '0'}, []),\n","  Annotation(token, 285, 293, presented, {'sentence': '0'}, []),\n","  Annotation(token, 295, 298, with, {'sentence': '0'}, []),\n","  Annotation(token, 300, 300, a, {'sentence': '0'}, []),\n","  Annotation(token, 302, 309, one-week, {'sentence': '0'}, []),\n","  Annotation(token, 311, 317, history, {'sentence': '0'}, []),\n","  Annotation(token, 319, 320, of, {'sentence': '0'}, []),\n","  Annotation(token, 322, 329, polyuria, {'sentence': '0'}, []),\n","  Annotation(token, 330, 330, ,, {'sentence': '0'}, []),\n","  Annotation(token, 332, 335, poor, {'sentence': '0'}, []),\n","  Annotation(token, 337, 344, appetite, {'sentence': '0'}, []),\n","  Annotation(token, 345, 345, ,, {'sentence': '0'}, []),\n","  Annotation(token, 347, 349, and, {'sentence': '0'}, []),\n","  Annotation(token, 351, 358, vomiting, {'sentence': '0'}, []),\n","  Annotation(token, 359, 359, ., {'sentence': '0'}, []),\n","  Annotation(token, 361, 363, She, {'sentence': '1'}, []),\n","  Annotation(token, 365, 367, was, {'sentence': '1'}, []),\n","  Annotation(token, 369, 370, on, {'sentence': '1'}, []),\n","  Annotation(token, 372, 380, metformin, {'sentence': '1'}, []),\n","  Annotation(token, 381, 381, ,, {'sentence': '1'}, []),\n","  Annotation(token, 383, 391, glipizide, {'sentence': '1'}, []),\n","  Annotation(token, 392, 392, ,, {'sentence': '1'}, []),\n","  Annotation(token, 394, 396, and, {'sentence': '1'}, []),\n","  Annotation(token, 398, 410, dapagliflozin, {'sentence': '1'}, []),\n","  Annotation(token, 412, 414, for, {'sentence': '1'}, []),\n","  Annotation(token, 416, 419, T2DM, {'sentence': '1'}, []),\n","  Annotation(token, 421, 423, and, {'sentence': '1'}, []),\n","  Annotation(token, 425, 436, atorvastatin, {'sentence': '1'}, []),\n","  Annotation(token, 438, 440, and, {'sentence': '1'}, []),\n","  Annotation(token, 442, 452, gemfibrozil, {'sentence': '1'}, []),\n","  Annotation(token, 454, 456, for, {'sentence': '1'}, []),\n","  Annotation(token, 458, 460, HTG, {'sentence': '1'}, []),\n","  Annotation(token, 461, 461, ., {'sentence': '1'}, []),\n","  Annotation(token, 463, 465, She, {'sentence': '2'}, []),\n","  Annotation(token, 467, 469, had, {'sentence': '2'}, []),\n","  Annotation(token, 471, 474, been, {'sentence': '2'}, []),\n","  Annotation(token, 476, 477, on, {'sentence': '2'}, []),\n","  Annotation(token, 479, 491, dapagliflozin, {'sentence': '2'}, []),\n","  Annotation(token, 493, 495, for, {'sentence': '2'}, []),\n","  Annotation(token, 497, 499, six, {'sentence': '2'}, []),\n","  Annotation(token, 501, 506, months, {'sentence': '2'}, []),\n","  Annotation(token, 508, 509, at, {'sentence': '2'}, []),\n","  Annotation(token, 511, 513, the, {'sentence': '2'}, []),\n","  Annotation(token, 515, 518, time, {'sentence': '2'}, []),\n","  Annotation(token, 520, 521, of, {'sentence': '2'}, []),\n","  Annotation(token, 523, 534, presentation, {'sentence': '2'}, []),\n","  Annotation(token, 535, 535, ., {'sentence': '2'}, []),\n","  Annotation(token, 537, 544, Physical, {'sentence': '3'}, []),\n","  Annotation(token, 546, 556, examination, {'sentence': '3'}, []),\n","  Annotation(token, 558, 559, on, {'sentence': '3'}, []),\n","  Annotation(token, 561, 572, presentation, {'sentence': '3'}, []),\n","  Annotation(token, 574, 576, was, {'sentence': '3'}, []),\n","  Annotation(token, 578, 588, significant, {'sentence': '3'}, []),\n","  Annotation(token, 590, 592, for, {'sentence': '3'}, []),\n","  Annotation(token, 594, 596, dry, {'sentence': '3'}, []),\n","  Annotation(token, 598, 601, oral, {'sentence': '3'}, []),\n","  Annotation(token, 603, 608, mucosa, {'sentence': '3'}, []),\n","  Annotation(token, 610, 610, ;, {'sentence': '3'}, []),\n","  Annotation(token, 612, 624, significantly, {'sentence': '3'}, []),\n","  Annotation(token, 626, 626, ,, {'sentence': '3'}, []),\n","  Annotation(token, 628, 630, her, {'sentence': '3'}, []),\n","  Annotation(token, 632, 640, abdominal, {'sentence': '3'}, []),\n","  Annotation(token, 642, 652, examination, {'sentence': '3'}, []),\n","  Annotation(token, 654, 656, was, {'sentence': '3'}, []),\n","  Annotation(token, 658, 663, benign, {'sentence': '3'}, []),\n","  Annotation(token, 665, 668, with, {'sentence': '3'}, []),\n","  Annotation(token, 670, 671, no, {'sentence': '3'}, []),\n","  Annotation(token, 673, 682, tenderness, {'sentence': '3'}, []),\n","  Annotation(token, 683, 683, ,, {'sentence': '3'}, []),\n","  Annotation(token, 685, 692, guarding, {'sentence': '3'}, []),\n","  Annotation(token, 693, 693, ,, {'sentence': '3'}, 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Annotation(token, 783, 792, creatinine, {'sentence': '4'}, []),\n","  Annotation(token, 794, 796, 0.4, {'sentence': '4'}, []),\n","  Annotation(token, 798, 802, mg/dL, {'sentence': '4'}, []),\n","  Annotation(token, 803, 803, ,, {'sentence': '4'}, []),\n","  Annotation(token, 805, 817, triglycerides, {'sentence': '4'}, []),\n","  Annotation(token, 819, 821, 508, {'sentence': '4'}, []),\n","  Annotation(token, 823, 827, mg/dL, {'sentence': '4'}, []),\n","  Annotation(token, 828, 828, ,, {'sentence': '4'}, []),\n","  Annotation(token, 830, 834, total, {'sentence': '4'}, []),\n","  Annotation(token, 836, 846, cholesterol, {'sentence': '4'}, []),\n","  Annotation(token, 848, 850, 122, {'sentence': '4'}, []),\n","  Annotation(token, 852, 856, mg/dL, {'sentence': '4'}, []),\n","  Annotation(token, 857, 857, ,, {'sentence': '4'}, []),\n","  Annotation(token, 859, 861, and, {'sentence': '4'}, []),\n","  Annotation(token, 863, 868, venous, {'sentence': '4'}, []),\n","  Annotation(token, 870, 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'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'mellitus', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 70, 78, diagnosed, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'diagnosed', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 80, 84, eight, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'eight', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 86, 90, years, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'years', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 92, 96, prior, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'prior', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 98, 99, to, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'to', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 101, 112, presentation, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'presentation', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 114, 116, and, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'and', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 118, 127, subsequent, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'subsequent', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 129, 132, type, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'type', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 134, 136, two, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'two', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 138, 145, diabetes, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'diabetes', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 147, 154, mellitus, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'mellitus', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 156, 156, (, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '(', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 157, 160, T2DM, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'T2DM', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 161, 162, ),, {'isOOV': 'true', 'pieceId': '-1', 'isWordStart': 'true', 'token': '),', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 164, 166, one, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'one', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 168, 172, prior, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'prior', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 174, 180, episode, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'episode', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 182, 183, of, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'of', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 185, 195, HTG-induced, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'HTG-induced', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 197, 208, pancreatitis, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'pancreatitis', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 210, 214, three, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'three', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 216, 220, years, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'years', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 222, 226, prior, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'prior', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 228, 229, to, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'to', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 231, 242, presentation, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'presentation', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 243, 243, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 245, 247, and, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'and', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 249, 258, associated, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'associated', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 260, 263, with, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'with', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 265, 266, an, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'an', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 268, 272, acute, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'acute', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 274, 282, hepatitis, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'hepatitis', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 283, 283, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 285, 293, presented, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'presented', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 295, 298, with, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'with', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 300, 300, a, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'a', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 302, 309, one-week, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'one-week', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 311, 317, history, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'history', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 319, 320, of, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'of', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 322, 329, polyuria, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'polyuria', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 330, 330, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 332, 335, poor, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'poor', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 337, 344, appetite, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'appetite', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 345, 345, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 347, 349, and, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'and', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 351, 358, vomiting, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'vomiting', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 359, 359, ., {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '.', 'sentence': '0'}, []),\n","  Annotation(word_embeddings, 361, 363, She, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'She', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 365, 367, was, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'was', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 369, 370, on, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'on', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 372, 380, metformin, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'metformin', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 381, 381, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 383, 391, glipizide, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'glipizide', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 392, 392, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 394, 396, and, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'and', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 398, 410, dapagliflozin, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'dapagliflozin', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 412, 414, for, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'for', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 416, 419, T2DM, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'T2DM', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 421, 423, and, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'and', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 425, 436, atorvastatin, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'atorvastatin', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 438, 440, and, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'and', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 442, 452, gemfibrozil, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'gemfibrozil', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 454, 456, for, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'for', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 458, 460, HTG, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'HTG', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 461, 461, ., {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '.', 'sentence': '1'}, []),\n","  Annotation(word_embeddings, 463, 465, She, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'She', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 467, 469, had, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'had', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 471, 474, been, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'been', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 476, 477, on, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'on', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 479, 491, dapagliflozin, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'dapagliflozin', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 493, 495, for, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'for', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 497, 499, six, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'six', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 501, 506, months, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'months', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 508, 509, at, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'at', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 511, 513, the, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'the', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 515, 518, time, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'time', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 520, 521, of, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'of', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 523, 534, presentation, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'presentation', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 535, 535, ., {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '.', 'sentence': '2'}, []),\n","  Annotation(word_embeddings, 537, 544, Physical, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'Physical', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 546, 556, examination, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'examination', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 558, 559, on, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'on', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 561, 572, presentation, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'presentation', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 574, 576, was, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'was', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 578, 588, significant, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'significant', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 590, 592, for, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'for', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 594, 596, dry, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'dry', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 598, 601, oral, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'oral', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 603, 608, mucosa, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'mucosa', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 610, 610, ;, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ';', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 612, 624, significantly, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'significantly', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 626, 626, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 628, 630, her, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'her', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 632, 640, abdominal, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'abdominal', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 642, 652, examination, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'examination', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 654, 656, was, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'was', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 658, 663, benign, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'benign', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 665, 668, with, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'with', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 670, 671, no, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'no', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 673, 682, tenderness, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'tenderness', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 683, 683, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 685, 692, guarding, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'guarding', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 693, 693, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 695, 696, or, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'or', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 698, 705, rigidity, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'rigidity', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 706, 706, ., {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '.', 'sentence': '3'}, []),\n","  Annotation(word_embeddings, 708, 716, Pertinent, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'Pertinent', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 718, 727, laboratory, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'laboratory', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 729, 736, findings, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'findings', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 738, 739, on, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'on', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 741, 749, admission, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'admission', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 751, 754, were, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'were', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 755, 755, :, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ':', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 757, 761, serum, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'serum', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 763, 769, glucose, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'glucose', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 771, 773, 111, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '111', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 775, 779, mg/dl, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'mg/dl', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 780, 780, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 783, 792, creatinine, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'creatinine', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 794, 796, 0.4, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '0.4', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 798, 802, mg/dL, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'mg/dL', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 803, 803, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 805, 817, triglycerides, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'triglycerides', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 819, 821, 508, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '508', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 823, 827, mg/dL, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'mg/dL', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 828, 828, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 830, 834, total, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'total', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 836, 846, cholesterol, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'cholesterol', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 848, 850, 122, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '122', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 852, 856, mg/dL, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'mg/dL', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 857, 857, ,, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': ',', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 859, 861, and, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'and', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 863, 868, venous, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'venous', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 870, 871, pH, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': 'pH', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 873, 876, 7.27, {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '7.27', 'sentence': '4'}, []),\n","  Annotation(word_embeddings, 877, 877, ., {'isOOV': 'false', 'pieceId': '-1', 'isWordStart': 'true', 'token': '.', 'sentence': '4'}, [])],\n"," 'sentence': [Annotation(document, 1, 359, A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus (T2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, and associated with an acute hepatitis, presented with a one-week history of polyuria, poor appetite, and vomiting., {'sentence': '0'}, []),\n","  Annotation(document, 361, 461, She was on metformin, glipizide, and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG., {'sentence': '1'}, []),\n","  Annotation(document, 463, 535, She had been on dapagliflozin for six months at the time of presentation., {'sentence': '2'}, []),\n","  Annotation(document, 537, 706, Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness, guarding, or rigidity., {'sentence': '3'}, []),\n","  Annotation(document, 708, 877, Pertinent laboratory findings on admission were: serum glucose 111 mg/dl,  creatinine 0.4 mg/dL, triglycerides 508 mg/dL, total cholesterol 122 mg/dL, and venous pH 7.27., {'sentence': '4'}, [])]}"]},"metadata":{},"execution_count":29}],"source":["light_result[0]"]},{"cell_type":"markdown","metadata":{"id":"3mZftbxBjgoH"},"source":["**Getting embeddings with LP**"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":651,"status":"ok","timestamp":1712935143925,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"kY3J_Mu-jdaS","outputId":"0b6b37f0-fec4-4602-9095-d8dfa345985e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('A',\n","  '0.040217556 0.4003962 0.69088835 -0.44649735 -0.40282863 -0.45621523 -0.20415598 0.13707675 -0.010474405 -0.42899358 -0.2879874 -0.03076839 -0.40367207 -0.32588458 0.23294163 -0.16012932 -0.35798857 0.043061644 -0.4091242 0.101141304 -0.61503464 0.068771854 -0.025053142 0.11466996 -0.57737786 -0.25603554 -0.6950373 0.3328502 -0.100428 0.0191588 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NLU**\n","\n"]},{"cell_type":"markdown","metadata":{"id":"5V_lvnPsKQxz"},"source":["John Snow Labs' NLU is a Python library for applying state-of-the-art text mining, directly on any dataframe, with a single line of code. We will use some functionalities of NLU to prettify our results."]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2052,"status":"ok","timestamp":1712935145970,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"ihg5PWSHX0KN","outputId":"563c86ec-b6b6-47a1-dd53-dd65f9c5394c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Warning::Spark Session already created, some configs may not take.\n","Warning::Spark Session already created, some configs may not take.\n"]},{"output_type":"execute_result","data":{"text/plain":["Index(['document', 'document_begin', 'document_end', 'entities_ner_chunk',\n","       'entities_ner_chunk_begin', 'entities_ner_chunk_class',\n","       'entities_ner_chunk_confidence', 'entities_ner_chunk_end',\n","       'entities_ner_chunk_origin_chunk', 'entities_ner_chunk_origin_sentence',\n","       'ner_iob_ner_begin', 'ner_iob_ner_end', 'sentence_dl',\n","       'sentence_dl_begin', 'sentence_dl_end', 'token_begin', 'token_end',\n","       'word_embedding_embeddings', 'word_embedding_embeddings_begin',\n","       'word_embedding_embeddings_end'],\n","      dtype='object')"]},"metadata":{},"execution_count":31}],"source":["nlp.nlu.to_pretty_df(model, text, positions=True, output_level='chunk').columns"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":800},"executionInfo":{"elapsed":1086,"status":"ok","timestamp":1712935147052,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"IQnM4kPXYLdB","outputId":"ba075648-0ebb-4170-a310-4b793f1da5bc"},"outputs":[{"output_type":"stream","name":"stdout","text":["Warning::Spark Session already created, some configs may not take.\n","Warning::Spark Session already created, some configs may not take.\n"]},{"output_type":"execute_result","data":{"text/plain":["                      entities_ner_chunk entities_ner_chunk_begin  \\\n","0          gestational diabetes mellitus                       40   \n","0  subsequent type two diabetes mellitus                      118   \n","0                                   T2DM                      157   \n","0               HTG-induced pancreatitis                      185   \n","0                     an acute hepatitis                      265   \n","0                               polyuria                      322   \n","0                          poor appetite                      332   \n","0                               vomiting                      351   \n","0                              metformin                      372   \n","0                              glipizide                      383   \n","0                          dapagliflozin                      398   \n","0                                   T2DM                      416   \n","0                           atorvastatin                      425   \n","0                            gemfibrozil                      442   \n","0                                    HTG                      458   \n","0                          dapagliflozin                      479   \n","0                   Physical examination                      537   \n","0                        dry oral mucosa                      594   \n","0              her abdominal examination                      628   \n","0                             tenderness                      673   \n","\n","  entities_ner_chunk_end entities_ner_chunk_origin_sentence  \\\n","0                     68                                  0   \n","0                    154                                  0   \n","0                    160                                  0   \n","0                    208                                  0   \n","0                    282                                  0   \n","0                    329                                  0   \n","0         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                 PROBLEM  \n","0                  PROBLEM  \n","0                  PROBLEM  \n","0                  PROBLEM  \n","0                  PROBLEM  \n","0                  PROBLEM  \n","0                  PROBLEM  \n","0                TREATMENT  \n","0                TREATMENT  \n","0                TREATMENT  \n","0                  PROBLEM  \n","0                TREATMENT  \n","0                TREATMENT  \n","0                  PROBLEM  \n","0                TREATMENT  \n","0                     TEST  \n","0                  PROBLEM  \n","0                     TEST  \n","0                  PROBLEM  "],"text/html":["\n","  <div id=\"df-6fd5fbe5-a503-4044-93ef-bf575b27946c\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: 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<td>460</td>\n","      <td>1</td>\n","      <td>PROBLEM</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>dapagliflozin</td>\n","      <td>479</td>\n","      <td>491</td>\n","      <td>2</td>\n","      <td>TREATMENT</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>Physical examination</td>\n","      <td>537</td>\n","      <td>556</td>\n","      <td>3</td>\n","      <td>TEST</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>dry oral mucosa</td>\n","      <td>594</td>\n","      <td>608</td>\n","      <td>3</td>\n","      <td>PROBLEM</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>her abdominal examination</td>\n","      <td>628</td>\n","      <td>652</td>\n","      <td>3</td>\n","      <td>TEST</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>tenderness</td>\n","      <td>673</td>\n","      <td>682</td>\n","      <td>3</td>\n","      <td>PROBLEM</td>\n","    </tr>\n","  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   },\n    {\n      \"column\": \"entities_ner_chunk_end\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 68,\n        \"max\": 871,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          380,\n          452,\n          391\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"entities_ner_chunk_origin_sentence\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"1\",\n          \"4\",\n          \"2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"entities_ner_chunk_class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"PROBLEM\",\n          \"TREATMENT\",\n          \"TEST\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":32}],"source":["#For a given pipeline output level is automatically set to the last anntator's output level by default.\n","#This can be changed by defining the value of \"output_level\" parameter.\n","#Values can be set as; token, sentence, document, chunk and relation .\n","\n","cols = [\n","     'entities_ner_chunk',\n","     'entities_ner_chunk_begin',\n","     'entities_ner_chunk_end',\n","     'entities_ner_chunk_origin_sentence',\n","     'entities_ner_chunk_class',\n","]\n","df_clinical = nlp.nlu.to_pretty_df(model, text, positions=True, output_level='chunk')[cols]\n","df_clinical.head(20)"]},{"cell_type":"markdown","metadata":{"id":"iQ1KUzkryBvz"},"source":["### NER Visualizer\n","\n","For saving the visualization result as html, provide `save_path` parameter in the display function."]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":751},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1712935147052,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"Jh1zoknFybBO","outputId":"b7c3481b-f9ff-4612-f3b3-ac4eebe29716"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","<style>\n","    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap');\n","    @import url('https://fonts.googleapis.com/css2?family=Vistol Regular:wght@300;400;500;600;700&display=swap');\n","    \n","    .spark-nlp-display-scroll-entities {\n","        border: 1px solid #E7EDF0;\n","        border-radius: 3px;\n","        text-align: justify;\n","        \n","    }\n","    .spark-nlp-display-scroll-entities span {  \n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #536B76;\n","        font-family: 'Montserrat', sans-serif !important;\n","    }\n","    \n","    .spark-nlp-display-entity-wrapper{\n","    \n","        display: inline-grid;\n","        text-align: center;\n","        border-radius: 4px;\n","        margin: 0 2px 5px 2px;\n","        padding: 1px\n","    }\n","    .spark-nlp-display-entity-name{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        background: #f1f2f3;\n","        border-width: medium;\n","        text-align: center;\n","        \n","        font-weight: 400;\n","        \n","        border-radius: 5px;\n","        padding: 2px 5px;\n","        display: block;\n","        margin: 3px 2px;\n","    \n","    }\n","    .spark-nlp-display-entity-type{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-entity-resolution{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Vistol Regular', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-others{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        font-weight: 400;\n","    }\n","\n","</style>\n"," <span class=\"spark-nlp-display-others\" style=\"background-color: white\"><br>A 28-year-old female with a history of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">gestational diabetes mellitus </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> diagnosed eight years prior to presentation and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">subsequent type two diabetes mellitus </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> (</span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">T2DM </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">), one prior episode of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">HTG-induced pancreatitis </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> three years prior to presentation, and associated with </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">an acute hepatitis </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, presented with a one-week history of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">polyuria </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">poor appetite </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">vomiting </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">.<br>She was on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">metformin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">glipizide </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">dapagliflozin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">T2DM </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">atorvastatin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">gemfibrozil </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">HTG </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">. She had been on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">dapagliflozin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for six months at the time of presentation.<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">Physical examination </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> on presentation was significant for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">dry oral mucosa </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> ; significantly , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">her abdominal examination </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was benign with no </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">tenderness </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">guarding </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, or </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">rigidity </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">. Pertinent laboratory findings on admission were: </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">serum glucose </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 111 mg/dl,  </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">creatinine </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 0.4 mg/dL, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">triglycerides </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 508 mg/dL, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">total cholesterol </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 122 mg/dL, and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">venous pH </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 7.27.<br></span></div>"]},"metadata":{}}],"source":["visualiser = nlp.viz.NerVisualizer()\n","\n","visualiser.display(light_result[0], label_col='ner_chunk', document_col='document', save_path=\"display_result.html\")\n","\n","\n","# Change color of an entity label\n","\n","# visualiser.set_label_colors({'PROBLEM':'#008080', 'TEST':'#800080', 'TREATMENT':'#808080'})\n","# visualiser.display(light_result[0], label_col='ner_chunk')\n","\n","\n","# Set label filter\n","\n","# visualiser.display(light_result[0], label_col='ner_chunk', document_col='document',\n","                   #labels=['PROBLEM','TEST'])"]},{"cell_type":"markdown","metadata":{"id":"XfsS_dlJGT1f"},"source":["### ModelTracer"]},{"cell_type":"markdown","metadata":{"id":"K1xVnq63Gatj"},"source":["`ModelTracer` allows to track the uids and timestamps of each stage of the pipeline.\n","\n","Let's apply it over an empty data with our existing pipeline."]},{"cell_type":"code","execution_count":34,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3226,"status":"ok","timestamp":1712935150272,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"36LR6KhPKfUT","outputId":"a22b9c9d-bcb9-47b6-de57-a374a96f5235"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----+------------------------------------------+--------+-----+----------+---+---------+----------------------------------------------------------------------+----------------------------------------------------------------------------+--------------------------------------------------------------+--------------------------------------------------------------------------+--------------------------------------------------------------------+-------------------------------------------------------------------------+\n","|text|document                                  |sentence|token|embeddings|ner|ner_chunk|documentassembler_model_uid                                           |sentencedetectordlmodel_model_uid                                           |tokenizer_model_uid                                           |word_embeddings_model_model_uid                                           |medicalnermodel_model_uid                                           |nerconverterinternal_model_uid                                           |\n","+----+------------------------------------------+--------+-----+----------+---+---------+----------------------------------------------------------------------+----------------------------------------------------------------------------+--------------------------------------------------------------+--------------------------------------------------------------------------+--------------------------------------------------------------------+-------------------------------------------------------------------------+\n","|    |[{document, 0, -1, , {sentence -> 0}, []}]|[]      |[]   |[]        |[] |[]       |{uid -> DocumentAssembler_b06127093d4f, timestamp -> 2024-04-12_15:19}|{uid -> SentenceDetectorDLModel_6bafc4746ea5, timestamp -> 2024-04-12_15:19}|{uid -> Tokenizer_b75717cb0f7a, timestamp -> 2024-04-12_15:19}|{uid -> WORD_EMBEDDINGS_MODEL_9004b1d00302, timestamp -> 2024-04-12_15:19}|{uid -> MedicalNerModel_1a8637089929, timestamp -> 2024-04-12_15:19}|{uid -> NerConverterInternal_20542571f649, timestamp -> 2024-04-12_15:19}|\n","+----+------------------------------------------+--------+-----+----------+---+---------+----------------------------------------------------------------------+----------------------------------------------------------------------------+--------------------------------------------------------------+--------------------------------------------------------------------------+--------------------------------------------------------------------+-------------------------------------------------------------------------+\n","\n"]}],"source":["data = spark.createDataFrame([['']]).toDF(\"text\")\n","\n","df = nlpPipeline.fit(data).transform(data)\n","\n","tracer_result = medical.ModelTracer().addUidCols(pipeline = nlpPipeline, df = df)\n","\n","tracer_result.show(truncate=False)"]},{"cell_type":"markdown","metadata":{"id":"zOXLMXIWRs_l"},"source":["## NER JSL\n","Let's show an example of `ner_jsl` model that has about 80 clinical entity labels by changing just only the model name.\n","\n","**Entities**\n","\n","| | | | | |\n","|-|-|-|-|-|\n","|Injury_or_Poisoning|Direction|Test|Admission_Discharge|Death_Entity|\n","|Relationship_Status|Duration|Respiration|Hyperlipidemia|Birth_Entity|\n","|Age|Labour_Delivery|Family_History_Header|BMI|Temperature|\n","|Alcohol|Kidney_Disease|Oncological|Medical_History_Header|Cerebrovascular_Disease|\n","|Oxygen_Therapy|O2_Saturation|Psychological_Condition|Heart_Disease|Employment|\n","|Obesity|Disease_Syndrome_Disorder|Pregnancy|ImagingFindings|Procedure|\n","|Medical_Device|Race_Ethnicity|Section_Header|Symptom|Treatment|\n","|Substance|Route|Drug_Ingredient|Blood_Pressure|Diet|\n","|External_body_part_or_region|LDL|VS_Finding|Allergen|EKG_Findings|\n","|Imaging_Technique|Triglycerides|RelativeTime|Gender|Pulse|\n","|Social_History_Header|Substance_Quantity|Diabetes|Modifier|Internal_organ_or_component|\n","|Clinical_Dept|Form|Drug_BrandName|Strength|Fetus_NewBorn|\n","|RelativeDate|Height|Test_Result|Sexually_Active_or_Sexual_Orientation|Frequency|\n","|Time|Weight|Vaccine|Vital_Signs_Header|Communicable_Disease|\n","|Dosage|Overweight|Hypertension|HDL|Total_Cholesterol|\n","|Smoking|Date||||"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13650,"status":"ok","timestamp":1712935163905,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"xzfAWMGjR3ov","outputId":"da40c72f-bf38-433a-e9a5-3443299aa6d1"},"outputs":[{"output_type":"stream","name":"stdout","text":["sentence_detector_dl_healthcare download started this may take some time.\n","Approximate size to download 367.3 KB\n","[OK!]\n","embeddings_clinical download started this may take some time.\n","Approximate size to download 1.6 GB\n","[OK!]\n","ner_jsl download started this may take some time.\n","[OK!]\n"]}],"source":["documentAssembler = nlp.DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","sentenceDetector = nlp.SentenceDetectorDLModel.pretrained(\"sentence_detector_dl_healthcare\",\"en\",\"clinical/models\") \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"sentence\")\n","\n","tokenizer = nlp.Tokenizer()\\\n","    .setInputCols([\"sentence\"])\\\n","    .setOutputCol(\"token\")\n","\n","word_embeddings = nlp.WordEmbeddingsModel.pretrained(\"embeddings_clinical\", \"en\", \"clinical/models\")\\\n","    .setInputCols([\"sentence\", \"token\"])\\\n","    .setOutputCol(\"embeddings\")\n","\n","jsl_ner = medical.NerModel.pretrained(\"ner_jsl\", \"en\", \"clinical/models\") \\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"]) \\\n","    .setOutputCol(\"jsl_ner\")\n","\n","jsl_ner_converter = medical.NerConverterInternal() \\\n","    .setInputCols([\"sentence\", \"token\", \"jsl_ner\"]) \\\n","    .setOutputCol(\"jsl_ner_chunk\")\n","\n","jsl_ner_pipeline = nlp.Pipeline(stages=[\n","    documentAssembler,\n","    sentenceDetector,\n","    tokenizer,\n","    word_embeddings,\n","    jsl_ner,\n","    jsl_ner_converter])\n","\n","empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n","\n","jsl_ner_model = jsl_ner_pipeline.fit(empty_data)\n"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"GfEYvGsXrouX","executionInfo":{"status":"ok","timestamp":1712935165576,"user_tz":-180,"elapsed":1674,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["jsl_light_model = nlp.LightPipeline(jsl_ner_model)\n","\n","jsl_light_result = jsl_light_model.fullAnnotate(text)"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":748},"executionInfo":{"elapsed":902,"status":"ok","timestamp":1712935166474,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"87JCd2EGP9FA","outputId":"eefe6b9c-6031-42a5-c1a6-2ba71a55745f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Warning::Spark Session already created, some configs may not take.\n","Warning::Spark Session already created, some configs may not take.\n"]},{"output_type":"execute_result","data":{"text/plain":["          entities_jsl_ner_chunk entities_jsl_ner_chunk_begin  \\\n","0                    28-year-old                            3   \n","0                         female                           15   \n","0  gestational diabetes mellitus                           40   \n","0              eight years prior                           80   \n","0     type two diabetes mellitus                          129   \n","0                           T2DM                          157   \n","0       HTG-induced pancreatitis                          185   \n","0              three years prior                          210   \n","0                          acute                          268   \n","0                      hepatitis                          274   \n","0                       one-week                          302   \n","0                       polyuria                          322   \n","0                  poor appetite                          332   \n","0                       vomiting                          351   \n","0                            She                          361   \n","0                      metformin                          372   \n","0                      glipizide                          383   \n","0                  dapagliflozin                          398   \n","0                           T2DM                          416   \n","0                   atorvastatin                          425   \n","\n","  entities_jsl_ner_chunk_end entities_jsl_ner_chunk_origin_sentence  \\\n","0                         13                                      0   \n","0                         20                                      0   \n","0                         68                                      0   \n","0                         96                                      0   \n","0                        154                                      0   \n","0                        160  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          1   \n","0                        436                                      1   \n","\n","  entities_jsl_ner_chunk_class  \n","0                          Age  \n","0                       Gender  \n","0                     Diabetes  \n","0                 RelativeDate  \n","0                     Diabetes  \n","0                     Diabetes  \n","0    Disease_Syndrome_Disorder  \n","0                 RelativeDate  \n","0                     Modifier  \n","0    Disease_Syndrome_Disorder  \n","0                     Duration  \n","0                      Symptom  \n","0                      Symptom  \n","0                      Symptom  \n","0                       Gender  \n","0              Drug_Ingredient  \n","0              Drug_Ingredient  \n","0              Drug_Ingredient  \n","0                     Diabetes  \n","0              Drug_Ingredient  "],"text/html":["\n","  <div id=\"df-7f21eb27-9896-44e6-98a3-089c0874b8dc\" class=\"colab-df-container\">\n","    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<tr>\n","      <th>0</th>\n","      <td>111 mg/dl</td>\n","      <td>771</td>\n","      <td>779</td>\n","      <td>4</td>\n","      <td>Test_Result</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>creatinine</td>\n","      <td>783</td>\n","      <td>792</td>\n","      <td>4</td>\n","      <td>Test</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>0.4 mg/dL</td>\n","      <td>794</td>\n","      <td>802</td>\n","      <td>4</td>\n","      <td>Test_Result</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>triglycerides</td>\n","      <td>805</td>\n","      <td>817</td>\n","      <td>4</td>\n","      <td>Triglycerides</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>508 mg/dL</td>\n","      <td>819</td>\n","      <td>827</td>\n","      <td>4</td>\n","      <td>Test_Result</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>total cholesterol 122 mg/dL</td>\n","      <td>830</td>\n","      <td>856</td>\n","   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      font-weight: 400;\n","    }\n","\n","</style>\n"," <span class=\"spark-nlp-display-others\" style=\"background-color: white\"><br>A 28-year-old female with a history of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">gestational diabetes mellitus </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> diagnosed eight years prior to presentation and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">subsequent type two diabetes mellitus </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> (</span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">T2DM </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">), one prior episode of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">HTG-induced pancreatitis </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> three years prior to presentation, and associated with </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">an acute hepatitis </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, presented with a one-week history of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">polyuria </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">poor appetite </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">vomiting </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">.<br>She was on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">metformin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">glipizide </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">dapagliflozin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">T2DM </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">atorvastatin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">gemfibrozil </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">HTG </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">. She had been on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">dapagliflozin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for six months at the time of presentation.<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">Physical examination </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> on presentation was significant for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">dry oral mucosa </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> ; significantly , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">her abdominal examination </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was benign with no </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">tenderness </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">guarding </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, or </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">rigidity </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">. Pertinent laboratory findings on admission were: </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">serum glucose </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 111 mg/dl,  </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">creatinine </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 0.4 mg/dL, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">triglycerides </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 508 mg/dL, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">total cholesterol </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 122 mg/dL, and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">venous pH </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 7.27.<br></span></div>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"0c5f7c62-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"0c5b3292-f8e0-11ee-89e5-0242ac1c000c\"]);\n","//# sourceURL=js_41d8e55075"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"0c621616-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n","//# sourceURL=js_a32fcad31d"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"0c6285f6-f8e0-11ee-89e5-0242ac1c000c\"] = document.querySelector(\"#id1_content_3\");\n","//# sourceURL=js_cdfad54050"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"0c6301c0-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"0c6285f6-f8e0-11ee-89e5-0242ac1c000c\"]);\n","//# sourceURL=js_1698dd1450"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"0c636f34-f8e0-11ee-89e5-0242ac1c000c\"] = window[\"id1\"].setSelectedTabIndex(3);\n","//# sourceURL=js_a6a8a26909"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","<style>\n","    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap');\n","    @import url('https://fonts.googleapis.com/css2?family=Vistol Regular:wght@300;400;500;600;700&display=swap');\n","    \n","    .spark-nlp-display-scroll-entities {\n","        border: 1px solid #E7EDF0;\n","        border-radius: 3px;\n","        text-align: justify;\n","        \n","    }\n","    .spark-nlp-display-scroll-entities span {  \n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #536B76;\n","        font-family: 'Montserrat', sans-serif !important;\n","    }\n","    \n","    .spark-nlp-display-entity-wrapper{\n","    \n","        display: inline-grid;\n","        text-align: center;\n","        border-radius: 4px;\n","        margin: 0 2px 5px 2px;\n","        padding: 1px\n","    }\n","    .spark-nlp-display-entity-name{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        background: #f1f2f3;\n","        border-width: medium;\n","        text-align: center;\n","        \n","        font-weight: 400;\n","        \n","        border-radius: 5px;\n","        padding: 2px 5px;\n","        display: block;\n","        margin: 3px 2px;\n","    \n","    }\n","    .spark-nlp-display-entity-type{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-entity-resolution{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Vistol Regular', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-others{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        font-weight: 400;\n","    }\n","\n","</style>\n"," <span class=\"spark-nlp-display-others\" style=\"background-color: white\"><br>A </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #ffe0ac\"><span class=\"spark-nlp-display-entity-name\">28-year-old </span><span class=\"spark-nlp-display-entity-type\">Age</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #ffacb7\"><span class=\"spark-nlp-display-entity-name\">female </span><span class=\"spark-nlp-display-entity-type\">Gender</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> with a history of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #5BB49A\"><span class=\"spark-nlp-display-entity-name\">gestational diabetes mellitus </span><span class=\"spark-nlp-display-entity-type\">Diabetes</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> diagnosed </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #654BAE\"><span class=\"spark-nlp-display-entity-name\">eight years prior </span><span class=\"spark-nlp-display-entity-type\">RelativeDate</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> to presentation and subsequent </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #5BB49A\"><span class=\"spark-nlp-display-entity-name\">type two diabetes mellitus </span><span class=\"spark-nlp-display-entity-type\">Diabetes</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> (</span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #5BB49A\"><span class=\"spark-nlp-display-entity-name\">T2DM </span><span class=\"spark-nlp-display-entity-type\">Diabetes</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">), one prior episode of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #BA22AE\"><span class=\"spark-nlp-display-entity-name\">HTG-induced pancreatitis </span><span class=\"spark-nlp-display-entity-type\">Disease_Syndrome_Disorder</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #654BAE\"><span class=\"spark-nlp-display-entity-name\">three years prior </span><span class=\"spark-nlp-display-entity-type\">RelativeDate</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> to presentation, and associated with an </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B475D\"><span class=\"spark-nlp-display-entity-name\">acute </span><span class=\"spark-nlp-display-entity-type\">Modifier</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #BA22AE\"><span class=\"spark-nlp-display-entity-name\">hepatitis </span><span class=\"spark-nlp-display-entity-type\">Disease_Syndrome_Disorder</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, presented with a </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #21A828\"><span class=\"spark-nlp-display-entity-name\">one-week </span><span class=\"spark-nlp-display-entity-type\">Duration</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> history of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #C1279C\"><span class=\"spark-nlp-display-entity-name\">polyuria </span><span class=\"spark-nlp-display-entity-type\">Symptom</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #C1279C\"><span class=\"spark-nlp-display-entity-name\">poor appetite </span><span class=\"spark-nlp-display-entity-type\">Symptom</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #C1279C\"><span class=\"spark-nlp-display-entity-name\">vomiting </span><span class=\"spark-nlp-display-entity-type\">Symptom</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">.<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #ffacb7\"><span class=\"spark-nlp-display-entity-name\">She </span><span class=\"spark-nlp-display-entity-type\">Gender</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #2FA559\"><span class=\"spark-nlp-display-entity-name\">metformin </span><span class=\"spark-nlp-display-entity-type\">Drug_Ingredient</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #2FA559\"><span class=\"spark-nlp-display-entity-name\">glipizide </span><span class=\"spark-nlp-display-entity-type\">Drug_Ingredient</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #2FA559\"><span class=\"spark-nlp-display-entity-name\">dapagliflozin </span><span class=\"spark-nlp-display-entity-type\">Drug_Ingredient</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #5BB49A\"><span class=\"spark-nlp-display-entity-name\">T2DM </span><span class=\"spark-nlp-display-entity-type\">Diabetes</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #2FA559\"><span class=\"spark-nlp-display-entity-name\">atorvastatin </span><span class=\"spark-nlp-display-entity-type\">Drug_Ingredient</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #2FA559\"><span class=\"spark-nlp-display-entity-name\">gemfibrozil </span><span class=\"spark-nlp-display-entity-type\">Drug_Ingredient</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #1406B8\"><span class=\"spark-nlp-display-entity-name\">HTG </span><span class=\"spark-nlp-display-entity-type\">Hyperlipidemia</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">. </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #ffacb7\"><span class=\"spark-nlp-display-entity-name\">She </span><span class=\"spark-nlp-display-entity-type\">Gender</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> had been on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #2FA559\"><span class=\"spark-nlp-display-entity-name\">dapagliflozin </span><span class=\"spark-nlp-display-entity-type\">Drug_Ingredient</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #21A828\"><span class=\"spark-nlp-display-entity-name\">for six months </span><span class=\"spark-nlp-display-entity-type\">Duration</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> at the time of presentation.<br>Physical examination on presentation was significant for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #C1279C\"><span class=\"spark-nlp-display-entity-name\">dry oral mucosa </span><span class=\"spark-nlp-display-entity-type\">Symptom</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> ; significantly , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #ffacb7\"><span class=\"spark-nlp-display-entity-name\">her </span><span class=\"spark-nlp-display-entity-type\">Gender</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #0E8F8B\"><span class=\"spark-nlp-display-entity-name\">abdominal </span><span class=\"spark-nlp-display-entity-type\">External_body_part_or_region</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> examination was benign with no </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #C1279C\"><span class=\"spark-nlp-display-entity-name\">tenderness </span><span class=\"spark-nlp-display-entity-type\">Symptom</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #C1279C\"><span class=\"spark-nlp-display-entity-name\">guarding </span><span class=\"spark-nlp-display-entity-type\">Symptom</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, or </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #C1279C\"><span class=\"spark-nlp-display-entity-name\">rigidity </span><span class=\"spark-nlp-display-entity-type\">Symptom</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">. Pertinent laboratory findings on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #A46E8B\"><span class=\"spark-nlp-display-entity-name\">admission </span><span class=\"spark-nlp-display-entity-type\">Admission_Discharge</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> were: </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">serum glucose </span><span class=\"spark-nlp-display-entity-type\">Test</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #833B52\"><span class=\"spark-nlp-display-entity-name\">111 mg/dl </span><span class=\"spark-nlp-display-entity-type\">Test_Result</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">,  </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">creatinine </span><span class=\"spark-nlp-display-entity-type\">Test</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #833B52\"><span class=\"spark-nlp-display-entity-name\">0.4 mg/dL </span><span class=\"spark-nlp-display-entity-type\">Test_Result</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #5CB17E\"><span class=\"spark-nlp-display-entity-name\">triglycerides </span><span class=\"spark-nlp-display-entity-type\">Triglycerides</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #833B52\"><span class=\"spark-nlp-display-entity-name\">508 mg/dL </span><span class=\"spark-nlp-display-entity-type\">Test_Result</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #7B1A73\"><span class=\"spark-nlp-display-entity-name\">total cholesterol 122 mg/dL </span><span class=\"spark-nlp-display-entity-type\">Total_Cholesterol</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">venous pH </span><span class=\"spark-nlp-display-entity-type\">Test</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #833B52\"><span class=\"spark-nlp-display-entity-name\">7.27 </span><span class=\"spark-nlp-display-entity-type\">Test_Result</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">.<br></span></div>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"0c676be8-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"0c621616-f8e0-11ee-89e5-0242ac1c000c\"]);\n","//# sourceURL=js_c9ec185208"]},"metadata":{}}],"source":["from google.colab import widgets\n","\n","t = widgets.TabBar([\"ner_clinical_large\", \"ner_jsl\", \"viz_clinical\", \"viz_jsl\"])\n","\n","with t.output_to(0):\n","    display(df_clinical)\n","\n","with t.output_to(1):\n","    display(jsl_df)\n","\n","with t.output_to(2):\n","    visualiser.display(light_result[0], label_col='ner_chunk', document_col='document')\n","\n","with t.output_to(3):\n","    visualiser.display(jsl_light_result[0], label_col='jsl_ner_chunk', document_col='document')"]},{"cell_type":"markdown","metadata":{"id":"NZ_3PK8iyPIV"},"source":["## Posology NER\n","**Entities**\n","\n","- DOSAGE\n","- DRUG\n","- DURATION\n","- FORM\n","- FREQUENCY\n","- ROUTE\n","- STRENGTH"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22722,"status":"ok","timestamp":1712935189683,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"qDJDk1o41jfH","outputId":"19fae6a6-d766-4df4-b25c-e4db6471746d"},"outputs":[{"output_type":"stream","name":"stdout","text":["ner_posology download started this may take some time.\n","[OK!]\n","ner_posology_greedy download started this may take some time.\n","[OK!]\n"]}],"source":["# NER model trained on i2b2 (sampled from MIMIC) dataset\n","posology_ner = medical.NerModel.pretrained(\"ner_posology\", \"en\", \"clinical/models\") \\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"]) \\\n","    .setOutputCol(\"ner\")\n","\n","posology_ner_converter = medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\",\"token\",\"ner\"])\\\n","    .setOutputCol(\"ner_chunk\")\n","\n","# greedy model\n","posology_ner_greedy = medical.NerModel.pretrained(\"ner_posology_greedy\", \"en\", \"clinical/models\") \\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"]) \\\n","    .setOutputCol(\"ner_greedy\")\n","\n","ner_converter_greedy = medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\",\"token\",\"ner_greedy\"])\\\n","    .setOutputCol(\"ner_chunk_greedy\")\n","\n","nlpPipeline = nlp.Pipeline(stages=[\n","    documentAssembler,\n","    sentenceDetector,\n","    tokenizer,\n","    word_embeddings,\n","    posology_ner,\n","    posology_ner_converter,\n","    posology_ner_greedy,\n","    ner_converter_greedy])\n","\n","empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n","\n","posology_model = nlpPipeline.fit(empty_data)\n"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1712935189683,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"tGlKLjiJ1pk3","outputId":"97cf837e-691c-4000-f785-3b659b24511e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['O',\n"," 'B-DOSAGE',\n"," 'B-STRENGTH',\n"," 'I-STRENGTH',\n"," 'B-ROUTE',\n"," 'B-FREQUENCY',\n"," 'I-FREQUENCY',\n"," 'B-DRUG',\n"," 'I-DRUG',\n"," 'B-FORM',\n"," 'I-DOSAGE',\n"," 'B-DURATION',\n"," 'I-DURATION',\n"," 'I-FORM',\n"," 'I-ROUTE']"]},"metadata":{},"execution_count":40}],"source":["posology_ner.getClasses()"]},{"cell_type":"code","execution_count":41,"metadata":{"id":"dLi2DHEpyepZ","executionInfo":{"status":"ok","timestamp":1712935189684,"user_tz":-180,"elapsed":4,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["posology_result = posology_model.transform(mt_samples_df).cache()"]},{"cell_type":"code","execution_count":42,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14557,"status":"ok","timestamp":1712935204237,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"OjeG2WLly_ym","outputId":"b311f465-9b2c-4f57-d89e-0e483fe3d36e"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|index|                text|            document|            sentence|               token|          embeddings|                 ner|           ner_chunk|          ner_greedy|    ner_chunk_greedy|\n","+-----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|    0|Sample Type / Med...|[{document, 0, 54...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 1609, 16...|[{named_entity, 0...|[{chunk, 1609, 16...|\n","|    1|Sample Type / Med...|[{document, 0, 32...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 742, 750...|[{named_entity, 0...|[{chunk, 742, 753...|\n","|    2|Sample Type / Med...|[{document, 0, 42...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 230, 240...|[{named_entity, 0...|[{chunk, 369, 385...|\n","|    3|Sample Type / Med...|[{document, 0, 20...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|                  []|[{named_entity, 0...|                  []|\n","|    4|Sample Type / Med...|[{document, 0, 34...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 1102, 11...|[{named_entity, 0...|[{chunk, 1102, 11...|\n","|    5|Sample Type / Med...|[{document, 0, 15...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|                  []|[{named_entity, 0...|[{chunk, 68, 84, ...|\n","|    6|Sample Type / Med...|[{document, 0, 25...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 1242, 12...|[{named_entity, 0...|[{chunk, 389, 398...|\n","|    7|Sample Type / Med...|[{document, 0, 93...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 667, 677...|[{named_entity, 0...|[{chunk, 667, 677...|\n","|    8|Sample Type / Med...|[{document, 0, 20...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 495, 505...|[{named_entity, 0...|[{chunk, 495, 513...|\n","|    9|Sample Type / Med...|[{document, 0, 19...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 524, 526...|[{named_entity, 0...|[{chunk, 1234, 12...|\n","+-----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","\n"]}],"source":["posology_result.show(10)"]},{"cell_type":"code","execution_count":43,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1337,"status":"ok","timestamp":1712935205572,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"pL_TMXSv1paw","outputId":"4067a022-69c7-4fd1-fdcb-d1f5ecda1996"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+---+\n","|index|                text|            document|            sentence|               token|          embeddings|                 ner|           ner_chunk|          ner_greedy|    ner_chunk_greedy| id|\n","+-----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+---+\n","|    0|Sample Type / Med...|[{document, 0, 54...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 1609, 16...|[{named_entity, 0...|[{chunk, 1609, 16...|  0|\n","|    1|Sample Type / Med...|[{document, 0, 32...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 742, 750...|[{named_entity, 0...|[{chunk, 742, 753...|  1|\n","|    2|Sample Type / Med...|[{document, 0, 42...|[{document, 0, 53...|[{token, 0, 5, Sa...|[{word_embeddings...|[{named_entity, 0...|[{chunk, 230, 240...|[{named_entity, 0...|[{chunk, 369, 385...|  2|\n","+-----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+---+\n","only showing top 3 rows\n","\n"]}],"source":["# This will return a new DF with all the columns + id\n","posology_result = posology_result.withColumn(\"id\", F.monotonically_increasing_id())\n","\n","posology_result.show(3)"]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712935205572,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"YK0prud8UoDh","outputId":"f7e04106-559c-40ea-d1e9-157c493a14d1"},"outputs":[{"output_type":"stream","name":"stdout","text":["+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+\n","|                                                                                              result|                                                                                              result|\n","+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Discharge, Sum...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, BCCa, Excision...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Anemia, -, Con...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Intensity-Modu...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Neck, Dissecti...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, HDR, Brachythe...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Biopsy, -, Cer...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Metastatic, Ov...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Breast, Radiat...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Name, :, Lymph, Node, E...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...|\n","+----------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------+\n","\n"]}],"source":["posology_result.select('token.result','ner.result').show(truncate=100)"]},{"cell_type":"code","execution_count":45,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712935205572,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"DHV5kfhOClV8","outputId":"3d468e3e-8766-41f0-d75c-bda04cc5c7ea"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------------------------------------------------------------------+--------------------------------------------------------------------------------+\n","|                                                                          result|                                                                          result|\n","+--------------------------------------------------------------------------------+--------------------------------------------------------------------------------+\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Nam...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Nam...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Nam...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Nam...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O...|\n","|[Sample, Type, /, Medical, Specialty, :, Hematology, -, Oncology, Sample, Nam...|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O...|\n","+--------------------------------------------------------------------------------+--------------------------------------------------------------------------------+\n","only showing top 5 rows\n","\n"]}],"source":["posology_result.select('token.result','ner.result').show(5, truncate=80)"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1712935205573,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"3aEoR8Y5y8kE","outputId":"7c3fbe42-d197-4bd8-e559-2d9ab924c85e"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------+-----------+----------+\n","|         token|  ner_label|confidence|\n","+--------------+-----------+----------+\n","|      Coumadin|     B-DRUG|    0.9989|\n","|             1| B-STRENGTH|    0.8618|\n","|            mg| I-STRENGTH|    0.5132|\n","|         daily|B-FREQUENCY|    0.9999|\n","|    Amiodarone|     B-DRUG|    0.9993|\n","|           100| B-STRENGTH|    0.8538|\n","|            mg| I-STRENGTH|    0.5216|\n","|           p.o|    B-ROUTE|    0.9992|\n","|         daily|B-FREQUENCY|    0.9928|\n","|      Coumadin|     B-DRUG|    0.9915|\n","|       Lovenox|     B-DRUG|    0.8314|\n","|            40| B-STRENGTH|    0.5199|\n","|            mg| I-STRENGTH|    0.5121|\n","|subcutaneously|    B-ROUTE|    0.9887|\n","|  chemotherapy|     B-DRUG|    0.9984|\n","|     cisplatin|     B-DRUG|    0.9257|\n","|            75| B-STRENGTH|    0.9574|\n","| mg/centimeter| I-STRENGTH|    0.5491|\n","|           109| B-STRENGTH|    0.5699|\n","|            mg| I-STRENGTH|    0.6355|\n","+--------------+-----------+----------+\n","only showing top 20 rows\n","\n"]}],"source":["from pyspark.sql import functions as F\n","\n","posology_result.select(F.explode(F.arrays_zip(posology_result.token.result,\n","                                              posology_result.ner.result,\n","                                              posology_result.ner.metadata)).alias(\"cols\")) \\\n","               .select(F.expr(\"cols['0']\").alias(\"token\"),\n","                       F.expr(\"cols['1']\").alias(\"ner_label\"),\n","                       F.expr(\"cols['2']['confidence']\").alias(\"confidence\"))\\\n","               .filter(\"ner_label!='O'\")\\\n","               .show(20, truncate=100)"]},{"cell_type":"code","execution_count":47,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":639,"status":"ok","timestamp":1712935206209,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"T3pH3xulzHfr","outputId":"fcec0484-af87-4aa8-cef1-6bebaaeb3792"},"outputs":[{"output_type":"stream","name":"stdout","text":["+---+-----------+----------------+-----+----+---------+----------+\n","|id |sentence_id|chunk           |begin|end |ner_label|confidence|\n","+---+-----------+----------------+-----+----+---------+----------+\n","|0  |33         |Coumadin        |1609 |1616|DRUG     |0.9989    |\n","|0  |33         |1 mg            |1618 |1621|STRENGTH |0.6875    |\n","|0  |33         |daily           |1623 |1627|FREQUENCY|0.9999    |\n","|0  |34         |Amiodarone      |1696 |1705|DRUG     |0.9993    |\n","|0  |34         |100 mg          |1707 |1712|STRENGTH |0.68770003|\n","|0  |34         |p.o             |1714 |1716|ROUTE    |0.9992    |\n","|0  |34         |daily           |1719 |1723|FREQUENCY|0.9928    |\n","|0  |58         |Coumadin        |2770 |2777|DRUG     |0.9915    |\n","|0  |60         |Lovenox         |2880 |2886|DRUG     |0.8314    |\n","|0  |60         |40 mg           |2888 |2892|STRENGTH |0.51600003|\n","|0  |60         |subcutaneously  |2894 |2907|ROUTE    |0.9887    |\n","|0  |72         |chemotherapy    |4436 |4447|DRUG     |0.9984    |\n","|0  |72         |cisplatin       |4475 |4483|DRUG     |0.9257    |\n","|0  |72         |75 mg/centimeter|4485 |4500|STRENGTH |0.75325   |\n","|0  |72         |109 mg          |4519 |4524|STRENGTH |0.6027    |\n","|0  |72         |IV              |4526 |4527|ROUTE    |0.9993    |\n","|0  |72         |piggyback       |4529 |4537|DRUG     |0.578     |\n","|0  |72         |over 2 hours    |4539 |4550|DURATION |0.7061    |\n","|0  |72         |September       |4555 |4563|DRUG     |0.9277    |\n","|0  |72         |Alimta          |4574 |4579|DRUG     |0.8845    |\n","+---+-----------+----------------+-----+----+---------+----------+\n","only showing top 20 rows\n","\n"]}],"source":["posology_result.select('id',F.explode(F.arrays_zip(posology_result.ner_chunk.result,\n","                                                   posology_result.ner_chunk.begin,\n","                                                   posology_result.ner_chunk.end,\n","                                                   posology_result.ner_chunk.metadata)).alias(\"cols\")) \\\n","               .select('id', F.expr(\"cols['3']['sentence']\").alias(\"sentence_id\"),\n","                             F.expr(\"cols['0']\").alias(\"chunk\"),\n","                             F.expr(\"cols['1']\").alias(\"begin\"),\n","                             F.expr(\"cols['2']\").alias(\"end\"),\n","                             F.expr(\"cols['3']['entity']\").alias(\"ner_label\"),\n","                             F.expr(\"cols['3']['confidence']\").alias(\"confidence\"))\\\n","                    .filter(\"ner_label!='O'\")\\\n","                    .show(truncate=False)"]},{"cell_type":"code","execution_count":48,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1712935206209,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"rMtx9HaN61Oy","outputId":"8e17f1b5-cfbe-4112-d020-ff9d293d4c1a"},"outputs":[{"output_type":"stream","name":"stdout","text":["+---+-----------+--------------------------------------------------------------+-----+----+---------+----------+\n","|id |sentence_id|chunk                                                         |begin|end |ner_label|confidence|\n","+---+-----------+--------------------------------------------------------------+-----+----+---------+----------+\n","|0  |33         |Coumadin 1 mg                                                 |1609 |1621|DRUG     |0.6126    |\n","|0  |33         |daily                                                         |1623 |1627|FREQUENCY|0.9962    |\n","|0  |34         |Amiodarone 100 mg p.o                                         |1696 |1716|DRUG     |0.64115   |\n","|0  |34         |daily                                                         |1719 |1723|FREQUENCY|0.9035    |\n","|0  |58         |Coumadin                                                      |2770 |2777|DRUG     |0.99      |\n","|0  |72         |chemotherapy                                                  |4436 |4447|DRUG     |0.9973    |\n","|0  |72         |cisplatin 75 mg/centimeter                                    |4475 |4500|DRUG     |0.6081667 |\n","|0  |72         |109 mg IV piggyback                                           |4519 |4537|DRUG     |0.57665   |\n","|0  |72         |over 2 hours                                                  |4539 |4550|DURATION |0.5085333 |\n","|0  |72         |Alimta 500 mg/ centimeter squared equaling 730 mg IV piggyback|4574 |4635|DRUG     |0.58996   |\n","|0  |74         |chemotherapy                                                  |4713 |4724|DRUG     |0.9905    |\n","|0  |74         |IV fluid and IV                                               |4814 |4828|DRUG     |0.7181    |\n","|0  |75         |heparin                                                       |4863 |4869|DRUG     |0.6508    |\n","|0  |76         |Zofran                                                        |4917 |4922|DRUG     |0.9919    |\n","|0  |76         |Phenergan                                                     |4925 |4933|DRUG     |0.9978    |\n","|0  |76         |Coumadin                                                      |4936 |4943|DRUG     |0.9989    |\n","|0  |76         |Lovenox                                                       |4950 |4956|DRUG     |0.9775    |\n","|0  |76         |Vicodin                                                       |4963 |4969|DRUG     |0.9946    |\n","|1  |11         |Xylocaine 2%                                                  |742  |753 |DRUG     |0.8614    |\n","|1  |11         |epinephrine was injected                                      |760  |783 |DRUG     |0.8004667 |\n","+---+-----------+--------------------------------------------------------------+-----+----+---------+----------+\n","only showing top 20 rows\n","\n"]}],"source":["posology_result.select('id',F.explode(F.arrays_zip(posology_result.ner_chunk_greedy.result,\n","                                                   posology_result.ner_chunk_greedy.begin,\n","                                                   posology_result.ner_chunk_greedy.end,\n","                                                   posology_result.ner_chunk_greedy.metadata)).alias(\"cols\")) \\\n","               .select('id', F.expr(\"cols['3']['sentence']\").alias(\"sentence_id\"),\n","                             F.expr(\"cols['0']\").alias(\"chunk\"),\n","                             F.expr(\"cols['1']\").alias(\"begin\"),\n","                             F.expr(\"cols['2']\").alias(\"end\"),\n","                             F.expr(\"cols['3']['entity']\").alias(\"ner_label\"),\n","                             F.expr(\"cols['3']['confidence']\").alias(\"confidence\"))\\\n","                    .filter(\"ner_label!='O'\")\\\n","                    .show(truncate=False)"]},{"cell_type":"code","execution_count":49,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":723,"status":"ok","timestamp":1712935206930,"user":{"displayName":"Monster 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'0.9906'}"]},"metadata":{},"execution_count":50}],"source":["posology_result.select('ner_chunk').take(2)[1][0][0].metadata"]},{"cell_type":"code","execution_count":51,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1712935206931,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"iZEj6SNg62Im","outputId":"20e21e15-4e84-43e7-ad91-7486b4668185"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('The', 'O'),\n"," ('patient', 'O'),\n"," ('was', 'O'),\n"," ('prescribed', 'O'),\n"," ('1', 'B-DOSAGE'),\n"," ('capsule', 'B-FORM'),\n"," ('of', 'O'),\n"," ('Advil', 'B-DRUG'),\n"," ('for', 'B-DURATION'),\n"," ('5', 'I-DURATION'),\n"," ('days', 'I-DURATION'),\n"," ('.', 'O'),\n"," ('He', 'O'),\n"," ('was', 'O'),\n"," ('seen', 'O'),\n"," ('by', 'O'),\n"," ('the', 'O'),\n"," ('endocrinology', 'O'),\n"," ('service', 'O'),\n"," ('and', 'O'),\n"," ('she', 'O'),\n"," ('was', 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'I-DURATION'),\n"," ('months', 'I-DURATION'),\n"," ('.', 'O')]"]},"metadata":{},"execution_count":51}],"source":["posology_light_model = nlp.LightPipeline(posology_model)\n","\n","text ='The patient was prescribed 1 capsule of Advil for 5 days . He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely for 3 months .'\n","\n","posology_light_result = posology_light_model.annotate(text)\n","\n","list(zip(posology_light_result['token'], posology_light_result['ner']))"]},{"cell_type":"code","execution_count":52,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712935206932,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"UwGbpOlEYQIQ","outputId":"5f2393d8-9073-4393-9198-0c31413fcc9b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('The', 'O'),\n"," ('patient', 'O'),\n"," ('was', 'O'),\n"," ('prescribed', 'O'),\n"," ('1', 'B-DRUG'),\n"," ('capsule', 'I-DRUG'),\n"," ('of', 'I-DRUG'),\n"," ('Advil', 'I-DRUG'),\n"," ('for', 'B-DURATION'),\n"," ('5', 'I-DURATION'),\n"," ('days', 'I-DURATION'),\n"," ('.', 'O'),\n"," ('He', 'O'),\n"," ('was', 'O'),\n"," ('seen', 'O'),\n"," ('by', 'O'),\n"," 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           DOSAGE                       27   \n","0            capsule                     FORM                       29   \n","0              Advil                     DRUG                       40   \n","0         for 5 days                 DURATION                       46   \n","0           40 units                   DOSAGE                      126   \n","0   insulin glargine                     DRUG                      138   \n","0           at night                FREQUENCY                      155   \n","0           12 units                   DOSAGE                      166   \n","0     insulin lispro                     DRUG                      178   \n","0         with meals                FREQUENCY                      193   \n","0          metformin                     DRUG                      210   \n","0            1000 mg                 STRENGTH                      220   \n","0    two times a day                FREQUENCY                      228   \n","0   SGLT2 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\"entities_ner_chunk_begin\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 27,\n        \"max\": 326,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          193,\n          220,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"entities_ner_chunk_end\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 27,\n        \"max\": 337,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          202,\n          226,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":53}],"source":["cols = [\n","        'entities_ner_chunk',\n","        'entities_ner_chunk_class',\n","        'entities_ner_chunk_begin',\n","        'entities_ner_chunk_end',\n","       ]\n","\n","posology_result_df = nlp.nlu.to_pretty_df(posology_light_model, text, positions=True, output_level='chunk')[cols]\n","posology_result_df.head(20)"]},{"cell_type":"code","execution_count":54,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":417},"executionInfo":{"elapsed":1106,"status":"ok","timestamp":1712935209765,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"EozCGm7gq4vF","outputId":"cafb0d7c-4b72-4fc1-fcc6-7af7e9bf8983"},"outputs":[{"output_type":"stream","name":"stdout","text":["Warning::Spark Session already created, some configs may not take.\n","Warning::Spark Session already created, some configs may not take.\n"]},{"output_type":"execute_result","data":{"text/plain":["      entities_ner_chunk_greedy entities_ner_chunk_greedy_class  \\\n","0            1 capsule of Advil                            DRUG   \n","0                    for 5 days                        DURATION   \n","0  40 units of insulin glargine                            DRUG   \n","0                      at night                       FREQUENCY   \n","0    12 units of insulin lispro                            DRUG   \n","0                    with meals                       FREQUENCY   \n","0             metformin 1000 mg                            DRUG   \n","0               two times a day                       FREQUENCY   \n","0              SGLT2 inhibitors                            DRUG   \n","0                  for 3 months                        DURATION   \n","\n","  entities_ner_chunk_greedy_begin entities_ner_chunk_greedy_end  \n","0                              27                            44  \n","0                              46                            55  \n","0                             126                           153  \n","0                             155                           162  \n","0                             166                           191  \n","0                             193                           202  \n","0                             210                           226  \n","0                             228                           242  \n","0                             273                           288  \n","0                             326                           337  "],"text/html":["\n","  <div id=\"df-dd26fb15-cf17-4ad2-9fa5-80e592195a9a\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>entities_ner_chunk_greedy</th>\n","      <th>entities_ner_chunk_greedy_class</th>\n","      <th>entities_ner_chunk_greedy_begin</th>\n","      <th>entities_ner_chunk_greedy_end</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1 capsule of Advil</td>\n","      <td>DRUG</td>\n","      <td>27</td>\n","      <td>44</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>for 5 days</td>\n","      <td>DURATION</td>\n","      <td>46</td>\n","      <td>55</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>40 units of insulin glargine</td>\n","      <td>DRUG</td>\n","      <td>126</td>\n","      <td>153</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>at night</td>\n","      <td>FREQUENCY</td>\n","      <td>155</td>\n","      <td>162</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>12 units of insulin lispro</td>\n","      <td>DRUG</td>\n","      <td>166</td>\n","      <td>191</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>with meals</td>\n","      <td>FREQUENCY</td>\n","      <td>193</td>\n","      <td>202</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>metformin 1000 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h.ka=function(a){if(this.isEnabled()&&(R(this,2)&&P(this,!0),Ob(a)&&!(y&&z&&a.ctrlKey))){R(this,4)&&qd(this,!0);var b;if(b=this.B){var c;b=this.v&32&&(c=this.f())?sb(c)&&tb(c):!1}b&&this.f().focus()}!Ob(a)||y&&z&&a.ctrlKey||a.g()};h.la=function(a){this.isEnabled()&&(R(this,2)&&P(this,!0),this.o&4&&ud(this,a)&&R(this,4)&&qd(this,!1))};h.Ja=function(a){this.isEnabled()&&ud(this,a)};
var ud=function(a,b){if(R(a,16)){var c=!(a.o&16);O(a,16,c)&&Q(a,16,c)}R(a,8)&&rd(a,!0);R(a,64)&&sd(a,!(a.o&64));c=new E("action",a);b&&(c.altKey=b.altKey,c.ctrlKey=b.ctrlKey,c.metaKey=b.metaKey,c.shiftKey=b.shiftKey,c.A=b.A);return uc(a,c)};N.prototype.Na=function(){R(this,32)&&O(this,32,!0)&&Q(this,32,!0)};N.prototype.Fa=function(){R(this,4)&&qd(this,!1);R(this,32)&&O(this,32,!1)&&Q(this,32,!1)};
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pd.prototype.h=function(){this.a=!1};pd.prototype.j=function(){this.a=!0};var xd=function(a,b){if(!wd)return a.button=0,a.type=b,a;var c=document.createEvent("MouseEvents");c.initMouseEvent(b,a.bubbles,a.cancelable,a.view||null,a.detail,a.screenX,a.screenY,a.clientX,a.clientY,a.ctrlKey,a.altKey,a.shiftKey,a.metaKey,0,a.relatedTarget||null);return c};
pd.prototype.g=function(a){if(this.a)this.a=!1;else{var b=a.b,c=b.button,d=b.type,e=xd(b,"mousedown");this.b.ka(new G(e,a.a));e=xd(b,"mouseup");this.b.la(new G(e,a.a));wd||(b.button=c,b.type=d)}};pd.prototype.u=function(){this.b=null;pd.i.u.call(this)};var S=function(a,b,c){K.call(this,c);this.da=b||Xc.R();this.H=a||"vertical"};n(S,K);h=S.prototype;h.Da=null;h.ca=null;h.da=null;h.H=null;h.T=!0;h.O=!0;h.l=-1;h.s=null;h.V=!1;h.I=null;var yd=function(a){return a.Da||a.f()};h=S.prototype;h.ia=function(){this.a=this.j.b("DIV",this.da.c(this).join(" "))};h.ja=function(){return this.f()};h.xa=function(a){return"DIV"==a.tagName};h.va=function(a){this.a=$c(this.da,this,a);"none"==a.style.display&&(this.T=!1)};
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h.Xa=function(a){var b=Tc(this,a.target);if(-1<b&&b!=this.l){var c=L(this,this.l);c&&P(c,!1);this.l=b;c=L(this,this.l);this.V&&qd(c,!0);this.s&&c!=this.s&&(c.v&64?sd(c,!0):sd(this.s,!1))}b=this.f();q(b,"The DOM element for the container cannot be null.");null!=a.target.f()&&xb(b,"activedescendant",a.target.f().id)};h.cb=function(a){a.target==L(this,this.l)&&(this.l=-1);a=this.f();q(a,"The DOM element for the container cannot be null.");a.removeAttribute(wb("activedescendant"))};
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object>"],"application/javascript":["window[\"2693aa18-f8e0-11ee-89e5-0242ac1c000c\"] = window[\"id2\"].setSelectedTabIndex(1);\n","//# sourceURL=js_9bc62bb468"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["      entities_ner_chunk_greedy entities_ner_chunk_greedy_class  \\\n","0            1 capsule of Advil                            DRUG   \n","0                    for 5 days                        DURATION   \n","0  40 units of insulin glargine                            DRUG   \n","0                      at night                       FREQUENCY   \n","0    12 units of insulin lispro                            DRUG   \n","0                    with meals                       FREQUENCY   \n","0             metformin 1000 mg                            DRUG   \n","0               two times a day                       FREQUENCY   \n","0              SGLT2 inhibitors                            DRUG   \n","0                  for 3 months                        DURATION   \n","\n","  entities_ner_chunk_greedy_begin entities_ner_chunk_greedy_end  \n","0                              27                            44  \n","0                              46                            55  \n","0                             126                           153  \n","0                             155                           162  \n","0                             166                           191  \n","0                             193                           202  \n","0                             210                           226  \n","0                             228                           242  \n","0                             273                           288  \n","0                             326                           337  "],"text/html":["\n","  <div id=\"df-04a93bc4-c489-46d1-bc7a-4e5216b4d6c0\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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sourceURL=js_5c74cc0308"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"2699a21a-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n","//# sourceURL=js_4e0b1e5b10"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"269a0318-f8e0-11ee-89e5-0242ac1c000c\"] = document.querySelector(\"#id2_content_2\");\n","//# sourceURL=js_28557ae651"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"269a6524-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"269a0318-f8e0-11ee-89e5-0242ac1c000c\"]);\n","//# sourceURL=js_491e2d52d1"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"269acb54-f8e0-11ee-89e5-0242ac1c000c\"] = window[\"id2\"].setSelectedTabIndex(2);\n","//# sourceURL=js_32dc27fdbf"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","<style>\n","    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap');\n","    @import url('https://fonts.googleapis.com/css2?family=Vistol Regular:wght@300;400;500;600;700&display=swap');\n","    \n","    .spark-nlp-display-scroll-entities {\n","        border: 1px solid #E7EDF0;\n","        border-radius: 3px;\n","        text-align: justify;\n","        \n","    }\n","    .spark-nlp-display-scroll-entities span {  \n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #536B76;\n","        font-family: 'Montserrat', sans-serif !important;\n","    }\n","    \n","    .spark-nlp-display-entity-wrapper{\n","    \n","        display: inline-grid;\n","        text-align: center;\n","        border-radius: 4px;\n","        margin: 0 2px 5px 2px;\n","        padding: 1px\n","    }\n","    .spark-nlp-display-entity-name{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        background: #f1f2f3;\n","        border-width: medium;\n","        text-align: center;\n","        \n","        font-weight: 400;\n","        \n","        border-radius: 5px;\n","        padding: 2px 5px;\n","        display: block;\n","        margin: 3px 2px;\n","    \n","    }\n","    .spark-nlp-display-entity-type{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-entity-resolution{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Vistol Regular', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-others{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        font-weight: 400;\n","    }\n","\n","</style>\n"," <span class=\"spark-nlp-display-others\" style=\"background-color: white\">The patient was prescribed </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #3DB198\"><span class=\"spark-nlp-display-entity-name\">1 </span><span class=\"spark-nlp-display-entity-type\">DOSAGE</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #3424BB\"><span class=\"spark-nlp-display-entity-name\">capsule </span><span class=\"spark-nlp-display-entity-type\">FORM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">Advil </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #C60C17\"><span class=\"spark-nlp-display-entity-name\">for 5 days </span><span class=\"spark-nlp-display-entity-type\">DURATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> . He was seen by the endocrinology service and she was discharged on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #3DB198\"><span class=\"spark-nlp-display-entity-name\">40 units </span><span class=\"spark-nlp-display-entity-type\">DOSAGE</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">insulin glargine </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #658F85\"><span class=\"spark-nlp-display-entity-name\">at night </span><span class=\"spark-nlp-display-entity-type\">FREQUENCY</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #3DB198\"><span class=\"spark-nlp-display-entity-name\">12 units </span><span class=\"spark-nlp-display-entity-type\">DOSAGE</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">insulin lispro </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #658F85\"><span class=\"spark-nlp-display-entity-name\">with meals </span><span class=\"spark-nlp-display-entity-type\">FREQUENCY</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">metformin </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #A2AB7A\"><span class=\"spark-nlp-display-entity-name\">1000 mg </span><span class=\"spark-nlp-display-entity-type\">STRENGTH</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #658F85\"><span class=\"spark-nlp-display-entity-name\">two times a day </span><span class=\"spark-nlp-display-entity-type\">FREQUENCY</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> . It was determined that all </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">SGLT2 inhibitors </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> should be discontinued indefinitely </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #C60C17\"><span class=\"spark-nlp-display-entity-name\">for 3 months </span><span class=\"spark-nlp-display-entity-type\">DURATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> .</span></div>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"269d13b4-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"2699a21a-f8e0-11ee-89e5-0242ac1c000c\"]);\n","//# sourceURL=js_264e1594b7"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"269fbefc-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n","//# sourceURL=js_300e868790"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"26a034d6-f8e0-11ee-89e5-0242ac1c000c\"] = document.querySelector(\"#id2_content_3\");\n","//# sourceURL=js_ed6498b6af"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"26a09c96-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"26a034d6-f8e0-11ee-89e5-0242ac1c000c\"]);\n","//# sourceURL=js_ac23a9de15"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"26a1049c-f8e0-11ee-89e5-0242ac1c000c\"] = window[\"id2\"].setSelectedTabIndex(3);\n","//# sourceURL=js_5d95bfc20d"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","<style>\n","    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap');\n","    @import url('https://fonts.googleapis.com/css2?family=Vistol Regular:wght@300;400;500;600;700&display=swap');\n","    \n","    .spark-nlp-display-scroll-entities {\n","        border: 1px solid #E7EDF0;\n","        border-radius: 3px;\n","        text-align: justify;\n","        \n","    }\n","    .spark-nlp-display-scroll-entities span {  \n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #536B76;\n","        font-family: 'Montserrat', sans-serif !important;\n","    }\n","    \n","    .spark-nlp-display-entity-wrapper{\n","    \n","        display: inline-grid;\n","        text-align: center;\n","        border-radius: 4px;\n","        margin: 0 2px 5px 2px;\n","        padding: 1px\n","    }\n","    .spark-nlp-display-entity-name{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        background: #f1f2f3;\n","        border-width: medium;\n","        text-align: center;\n","        \n","        font-weight: 400;\n","        \n","        border-radius: 5px;\n","        padding: 2px 5px;\n","        display: block;\n","        margin: 3px 2px;\n","    \n","    }\n","    .spark-nlp-display-entity-type{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-entity-resolution{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Vistol Regular', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-others{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        font-weight: 400;\n","    }\n","\n","</style>\n"," <span class=\"spark-nlp-display-others\" style=\"background-color: white\">The patient was prescribed </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">1 capsule of Advil </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #6F491B\"><span class=\"spark-nlp-display-entity-name\">for 5 days </span><span class=\"spark-nlp-display-entity-type\">DURATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> . He was seen by the endocrinology service and she was discharged on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">40 units of insulin glargine </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #3291C6\"><span class=\"spark-nlp-display-entity-name\">at night </span><span class=\"spark-nlp-display-entity-type\">FREQUENCY</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">12 units of insulin lispro </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #3291C6\"><span class=\"spark-nlp-display-entity-name\">with meals </span><span class=\"spark-nlp-display-entity-type\">FREQUENCY</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">metformin 1000 mg </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #3291C6\"><span class=\"spark-nlp-display-entity-name\">two times a day </span><span class=\"spark-nlp-display-entity-type\">FREQUENCY</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> . It was determined that all </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8B668B\"><span class=\"spark-nlp-display-entity-name\">SGLT2 inhibitors </span><span class=\"spark-nlp-display-entity-type\">DRUG</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> should be discontinued indefinitely </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #6F491B\"><span class=\"spark-nlp-display-entity-name\">for 3 months </span><span class=\"spark-nlp-display-entity-type\">DURATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> .</span></div>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["window[\"26a2fe3c-f8e0-11ee-89e5-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"269fbefc-f8e0-11ee-89e5-0242ac1c000c\"]);\n","//# sourceURL=js_b868fead1c"]},"metadata":{}}],"source":["posology_light_result= posology_light_model.fullAnnotate(text)\n","\n","from google.colab import widgets\n","\n","t = widgets.TabBar([\"ner_posology\", \"ner_posology_greedy\", \"viz_posology\", \"viz_posology_greedy\"])\n","\n","with t.output_to(0):\n","    display(posology_result_df.head(10))\n","\n","with t.output_to(1):\n","    display(posology_result_greedy_df.head(10))\n","\n","with t.output_to(2):\n","    visualiser.display(posology_light_result[0], label_col='ner_chunk', document_col='document')\n","\n","with t.output_to(3):\n","    visualiser.display(posology_light_result[0], label_col='ner_chunk_greedy', document_col='document')"]},{"cell_type":"markdown","metadata":{"id":"0vWIDpk50QfG"},"source":["## Writing a generic NER function"]},{"cell_type":"markdown","metadata":{"id":"mDXfsBokuq4h"},"source":["**Generic NER Function with LightPipeline**"]},{"cell_type":"code","execution_count":56,"metadata":{"id":"kd3wwfajfyDb","executionInfo":{"status":"ok","timestamp":1712935210811,"user_tz":-180,"elapsed":5,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["def get_light_model(embeddings, model_name = 'ner_clinical'):\n","\n","  documentAssembler = nlp.DocumentAssembler()\\\n","      .setInputCol(\"text\")\\\n","      .setOutputCol(\"document\")\n","\n","  sentenceDetector = nlp.SentenceDetector()\\\n","      .setInputCols([\"document\"])\\\n","      .setOutputCol(\"sentence\")\n","\n","  tokenizer = nlp.Tokenizer()\\\n","      .setInputCols([\"sentence\"])\\\n","      .setOutputCol(\"token\")\n","\n","  word_embeddings = nlp.WordEmbeddingsModel.pretrained(embeddings, \"en\", \"clinical/models\")\\\n","      .setInputCols([\"sentence\", \"token\"])\\\n","      .setOutputCol(\"embeddings\")\n","\n","  loaded_ner_model = medical.NerModel.pretrained(model_name, \"en\", \"clinical/models\") \\\n","      .setInputCols([\"sentence\", \"token\", \"embeddings\"]) \\\n","      .setOutputCol(\"ner\")\n","\n","  ner_converter = medical.NerConverterInternal() \\\n","      .setInputCols([\"sentence\", \"token\", \"ner\"]) \\\n","      .setOutputCol(\"ner_chunk\")\n","\n","  nlpPipeline = nlp.Pipeline(stages=[\n","      documentAssembler,\n","      sentenceDetector,\n","      tokenizer,\n","      word_embeddings,\n","      loaded_ner_model,\n","      ner_converter])\n","\n","  model = nlpPipeline.fit(spark.createDataFrame([[\"\"]]).toDF(\"text\"))\n","\n","  return nlp.LightPipeline(model)"]},{"cell_type":"code","execution_count":57,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9793,"status":"ok","timestamp":1712935220600,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"V81zaAe13qLU","outputId":"0feed353-7d13-44db-c190-d6b7c0b7e17a"},"outputs":[{"output_type":"stream","name":"stdout","text":["embeddings_clinical download started this may take some time.\n","Approximate size to download 1.6 GB\n","[OK!]\n","ner_clinical download started this may take some time.\n","[OK!]\n"]}],"source":["embeddings = 'embeddings_clinical'\n","\n","model_name = 'ner_clinical'\n","\n","light_model = get_light_model(embeddings, model_name)\n","\n","text = \"I had a headache yesterday and took an Advil.\""]},{"cell_type":"code","execution_count":58,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":167},"executionInfo":{"elapsed":1634,"status":"ok","timestamp":1712935222230,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"M53Wcu6pre0j","outputId":"232d981a-562b-49ea-ea7e-ccd72e7c9481"},"outputs":[{"output_type":"stream","name":"stdout","text":["Warning::Spark Session already created, some configs may not take.\n","Warning::Spark Session already created, some configs may not take.\n"]},{"output_type":"execute_result","data":{"text/plain":["  entities_ner_chunk_origin_sentence entities_ner_chunk_begin  \\\n","0                                  0                        6   \n","1                                  0                       36   \n","\n","  entities_ner_chunk_end entities_ner_chunk entities_ner_chunk_class  \n","0                     15         a headache                  PROBLEM  \n","1                     43           an Advil                TREATMENT  "],"text/html":["\n","  <div id=\"df-c6e54e45-2bba-4cfc-a0f9-7ac0ac60ccfa\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>entities_ner_chunk_origin_sentence</th>\n","      <th>entities_ner_chunk_begin</th>\n","      <th>entities_ner_chunk_end</th>\n","      <th>entities_ner_chunk</th>\n","      <th>entities_ner_chunk_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>15</td>\n","      <td>a headache</td>\n","      <td>PROBLEM</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>36</td>\n","      <td>43</td>\n","      <td>an Advil</td>\n","      <td>TREATMENT</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6e54e45-2bba-4cfc-a0f9-7ac0ac60ccfa')\"\n","           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43,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"entities_ner_chunk\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"an Advil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"entities_ner_chunk_class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"TREATMENT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":58}],"source":["cols = [\n","        'entities_ner_chunk_origin_sentence',\n","        'entities_ner_chunk_begin',\n","        'entities_ner_chunk_end',\n","        'entities_ner_chunk',\n","        'entities_ner_chunk_class',\n","       ]\n","\n","nlp.nlu.to_pretty_df(light_model, text, positions=True, output_level='chunk')[cols].reset_index(drop=True)"]},{"cell_type":"code","execution_count":59,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5345,"status":"ok","timestamp":1712935227570,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"LmLPlfCw5hh-","outputId":"681a18e5-f326-409e-84f8-5fb0f99ccd23"},"outputs":[{"output_type":"stream","name":"stdout","text":["embeddings_clinical download started this may take some time.\n","Approximate size to download 1.6 GB\n","[OK!]\n","ner_posology download started this may take some time.\n","[OK!]\n"]}],"source":["text ='''The patient was prescribed 1 capsule of Parol with meals .\n","He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day .\n","It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months .'''\n","\n","embeddings = 'embeddings_clinical'\n","\n","model_name = 'ner_posology'\n","\n","light_model = get_light_model(embeddings, model_name)"]},{"cell_type":"code","execution_count":60,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":543},"executionInfo":{"elapsed":2270,"status":"ok","timestamp":1712935229838,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"DLnWyjNrrm4v","outputId":"d9776df5-102f-4fd5-cf36-262a95fe9db2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Warning::Spark Session already created, some configs may not take.\n","Warning::Spark Session already created, some configs may not take.\n"]},{"output_type":"execute_result","data":{"text/plain":["   entities_ner_chunk_origin_sentence entities_ner_chunk_begin  \\\n","0                                   0                       27   \n","1                                   0                       29   \n","2                                   0                       40   \n","3                                   0                       46   \n","4                                   1                      126   \n","5                                   1                      138   \n","6                                   1                      155   \n","7                                   1                      166   \n","8                                   1                      178   \n","9                                   1                      193   \n","10                                  1                      210   \n","11                                  1                      220   \n","12                                  1                      228   \n","13                                  2                      273   \n","\n","   entities_ner_chunk_end entities_ner_chunk entities_ner_chunk_class  \n","0                      27                  1                   DOSAGE  \n","1                      35            capsule                     FORM  \n","2                      44              Parol                     DRUG  \n","3                      55         with meals                FREQUENCY  \n","4                     133           40 units                   DOSAGE  \n","5                     153   insulin glargine                     DRUG  \n","6                     162           at night                FREQUENCY  \n","7                     173           12 units                   DOSAGE  \n","8                     191     insulin lispro                     DRUG  \n","9                     202         with meals                FREQUENCY  \n","10                    218          metformin                     DRUG  \n","11                    226            1000 mg                 STRENGTH  \n","12                    242    two times a day                FREQUENCY  \n","13                    288   SGLT2 inhibitors                     DRUG  "],"text/html":["\n","  <div id=\"df-9441cb1b-967d-4596-b9cc-8a9df8f6cecb\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>entities_ner_chunk_origin_sentence</th>\n","      <th>entities_ner_chunk_begin</th>\n","      <th>entities_ner_chunk_end</th>\n","      <th>entities_ner_chunk</th>\n","      <th>entities_ner_chunk_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>27</td>\n","      <td>27</td>\n","      <td>1</td>\n","      <td>DOSAGE</td>\n","    </tr>\n","    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Her Coumadin was placed on hold.\n","She was instructed to followup with Dr. XYZ in the office to check her INR On August 24, 2007 .\"\"\"\n","\n","light_model = get_light_model(embeddings, model_name)"]},{"cell_type":"code","execution_count":62,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":323},"executionInfo":{"elapsed":1634,"status":"ok","timestamp":1712935240473,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"Z3claKyp6S_D","outputId":"bb0310ef-501e-4583-8bda-067fe567f950"},"outputs":[{"output_type":"stream","name":"stdout","text":["Warning::Spark Session already created, some configs may not take.\n","Warning::Spark Session already created, some configs may not take.\n"]},{"output_type":"execute_result","data":{"text/plain":["  entities_ner_chunk_origin_sentence entities_ner_chunk_begin  \\\n","0                                  0                        0   \n","1                                  0                       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We will show an example of `new_oncology_wip` model here.\n","\n","**Entities**\n","\n","| | | | |\n","|-|-|-|-|\n","| Invasion | Dosage | Hormonal_Therapy | Site_Lung |\n","| Biomarker_Result | Response_To_Treatment | Oncogene | Relative_Date |\n","| Site_Bone | Histological_Type | Radiation_Dose | Age |\n","| Duration | Grade | Tumor_Size | Line_Of_Therapy |\n","| Site_Breast | Cancer_Surgery | Smoking_Status | Imaging_Test |\n","| Adenopathy | Targeted_Therapy | Radiotherapy | Metastasis |\n","| Date | Cycle_Number | Pathology_Result | Cancer_Score |\n","| Cancer_Dx | Death_Entity | Site_Liver | Site_Brain |\n","| Pathology_Test | Chemotherapy | Biomarker | Frequency |\n","| Site_Lymph_Node | Performance_Status | Unspecific_Therapy | Cycle_Count |\n","| Race_Ethnicity | Gender | Staging | Immunotherapy |\n","| Site_Other_Body_Part | Tumor_Finding | Route | Cycle_Day |\n","| Direction| | | |\n","\n","You can check [Oncology Model Notebook](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb) for more examples.\n"]},{"cell_type":"code","execution_count":63,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712935240473,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"xhOvypXqSjfd","outputId":"c31dd468-f15e-4cd0-866c-bc6f0ba45f59"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Sample Type / Medical Specialty:\\nHematology - Oncology\\nSample Name:\\nDischarge Summary - Mesothelioma - 1\\nDescription:\\nMesothelioma, pleural effusion, atrial fibrillation, anemia, ascites, esophageal reflux, and history of deep venous thrombosis.\\n(Medical Transcription Sample Report)\\nPRINCIPAL DIAGNOSIS:\\nMesothelioma.\\nSECONDARY DIAGNOSES:\\nPleural effusion, atrial fibrillation, anemia, ascites, esophageal reflux, and history of deep venous thrombosis.\\nPROCEDURES\\n1. On August 24, 2007, decortication of the lung with pleural biopsy and transpleural fluoroscopy.\\n2. On August 20, 2007, thoracentesis.\\n3. On August 31, 2007, Port-A-Cath placement.\\nHISTORY AND PHYSICAL:\\nThe patient is a 41-year-old Vietnamese female with a nonproductive cough that started last week. She has had right-sided chest pain radiating to her back with fever starting yesterday. She has a history of pericarditis and pericardectomy in May 2006 and developed cough with right-sided chest pain, and went to an urgent care center. Chest x-ray revealed right-sided pleural effusion.\\nPAST MEDICAL HISTORY\\n1. Pericardectomy.\\n2. Pericarditis.\\n2. Atrial fibrillation.\\n4. RNCA with intracranial thrombolytic treatment.\\n5 PTA of MCA.\\n6. Mesenteric venous thrombosis.\\n7. Pericardial window.\\n8. Cholecystectomy.\\n9. Left thoracentesis.\\nFAMILY HISTORY:\\nNo family history of coronary artery disease, CVA, diabetes, CHF or MI. The patient has one family member, a sister, with history of cancer.\\nSOCIAL HISTORY:\\nShe is married. Employed with the US Post Office. She is a mother of three. Denies tobacco, alcohol or illicit drug use.\\nMEDICATIONS\\n1. Coumadin 1 mg daily. Last INR was on Tuesday, August 14, 2007, and her INR was 2.3.\\n2. Amiodarone 100 mg p.o. daily.\\nREVIEW OF SYSTEMS:\\nComplete review of systems negative except as in pulmonary as noted above. The patient also reports occasional numbness and tingling of her left arm.\\nPHYSICAL EXAMINATION\\nVITAL SIGNS: Blood pressure 123/95, heart rate 83, respirations 20, temperature 97, and oxygen saturation 97%.\\nGENERAL: Positive nonproductive cough and pain with coughing.\\nHEENT: Pupils are equal and reactive to light and accommodation. Tympanic membranes are clear.\\nNECK: Supple. No lymphadenopathy. No masses.\\nRESPIRATORY: Pleural friction rub is noted.\\nGI: Soft, nondistended, and nontender. Positive bowel sounds. No organomegaly.\\nEXTREMITIES: No edema, no clubbing, no cyanosis, no tenderness. Full range of motion. Normal pulses in all extremities.\\nSKIN: No breakdown or lesions. No ulcers.\\nNEUROLOGIC: Grossly intact. No focal deficits. Awake, alert, and oriented to person, place, and time.\\nLABORATORY DATA:\\nLabs are pending.\\nHOSPITAL COURSE:\\nThe patient was admitted for a right-sided pleural effusion for thoracentesis on Monday by Dr. X. Her Coumadin was placed on hold. A repeat echocardiogram was checked. She was started on prophylaxis for DVT with Lovenox 40 mg subcutaneously. Her history dated back to March 2005 when she first sought medical attention for evidence of pericarditis, which was treated with pericardial window in an outside hospital, at that time she was also found to have mesenteric pain and thrombosis, is now anticoagulated. Her pericardial fluid was accumulated and she was seen by Dr. Y. At that time, she was recommended for pericardectomy, which was performed by Dr. Z. Review of her CT scan from March 2006 prior to her pericardectomy, already shows bilateral plural effusions. The patient improved clinically after the pericardectomy with resolution of her symptoms. Recently, she was readmitted to the hospital with chest pain and found to have bilateral pleural effusion, the right greater than the left. CT of the chest also revealed a large mediastinal lymph node. We reviewed the pathology obtained from the pericardectomy in March 2006, which was diagnostic of mesothelioma. At this time, chest tube placement for drainage of the fluid occurred and thoracoscopy with fluid biopsies, which were performed, which revealed epithelioid malignant mesothelioma. The patient was then stained with a PET CT, which showed extensive uptake in the chest, bilateral pleural pericardial effusions, and lymphadenopathy. She also had acidic fluid, pectoral and intramammary lymph nodes and uptake in L4 with SUV of 4. This was consistent with stage III disease. Her repeat echocardiogram showed an ejection fraction of 45% to 49%. She was transferred to Oncology service and started on chemotherapy on September 1, 2007 with cisplatin 75 mg/centimeter squared equaling 109 mg IV piggyback over 2 hours on September 1, 2007, Alimta 500 mg/ centimeter squared equaling 730 mg IV piggyback over 10 minutes. This was all initiated after a Port-A-Cath was placed. The chemotherapy was well tolerated and the patient was discharged the following day after discontinuing IV fluid and IV. Her Port-A-Cath was packed with heparin according to protocol.\\nDISCHARGE MEDICATIONS:\\nZofran, Phenergan, Coumadin, and Lovenox, and Vicodin\\nDISCHARGE INSTRUCTIONS:\\nShe was instructed to followup with Dr. XYZ in the office to check her INR on Tuesday. She was instructed to call if she had any other questions or concerns in the interim.\\nKeywords:\\nhematology - oncology, mesothelioma, pleural effusion, atrial fibrillation, anemia, ascites, esophageal reflux, deep venous thrombosis, port-a-cath placement, port a cath, iv piggyback, venous thrombosis, atrial, thrombosis, pericardial, lymphadenopathy, fluid, pericardectomy, chest, pleural,'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":63}],"source":["mt_samples_df.limit(1).collect()[0][\"text\"]"]},{"cell_type":"code","execution_count":64,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11970,"status":"ok","timestamp":1712935252439,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"y1Ffv9qT_AYV","outputId":"9d08a093-4d4f-4c31-b22b-c227ceac8dae"},"outputs":[{"output_type":"stream","name":"stdout","text":["embeddings_clinical download started this may take some time.\n","Approximate size to download 1.6 GB\n","[OK!]\n","ner_oncology download started this may take some time.\n","[OK!]\n"]}],"source":["embeddings = 'embeddings_clinical'\n","\n","model_name = 'ner_oncology'\n","\n","text =  mt_samples_df.limit(1).collect()[0][\"text\"]\n","\n","light_model = get_light_model(embeddings, model_name)\n"]},{"cell_type":"code","execution_count":65,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":748},"executionInfo":{"elapsed":4259,"status":"ok","timestamp":1712935256686,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"0jIPze7e48Of","outputId":"06458ad6-0336-40ec-b466-fbd21e6984ea"},"outputs":[{"output_type":"stream","name":"stdout","text":["Warning::Spark Session already created, some configs may not take.\n","Warning::Spark Session already created, some configs may not take.\n"]},{"output_type":"execute_result","data":{"text/plain":["   entities_ner_chunk_origin_sentence 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NerChunker would be quite handy to extract entity groups with neighboring tokens when there is no pretrained NER model to address certain issues. Lets say we want to extract drug and frequency together as a single chunk even if there are some unwanted tokens between them."]},{"cell_type":"code","execution_count":66,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3629,"status":"ok","timestamp":1712935260303,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"WdK6LdDxvVSt","outputId":"bb68963c-3328-40e3-887c-a5423aafb699"},"outputs":[{"output_type":"stream","name":"stdout","text":["ner_posology download started this may take some time.\n","[OK!]\n"]}],"source":["posology_ner = medical.NerModel.pretrained(\"ner_posology\", \"en\", \"clinical/models\") \\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"]) \\\n","    .setOutputCol(\"ner\")\n","\n","ner_chunker = medical.NerChunker()\\\n","    .setInputCols([\"sentence\",\"ner\"])\\\n","    .setOutputCol(\"ner_chunk\")\\\n","    .setRegexParsers([\"<DRUG>.*<FREQUENCY>\"])\n","\n","nlpPipeline = nlp.Pipeline(stages=[\n","    documentAssembler,\n","    sentenceDetector,\n","    tokenizer,\n","    word_embeddings,\n","    posology_ner,\n","    ner_chunker])\n","\n","empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n","\n","ner_chunker_model = nlpPipeline.fit(empty_data)"]},{"cell_type":"code","execution_count":67,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1712935260304,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"wseg1Ko2vgKQ","outputId":"867ff310-2100-4f97-e44d-1df564b99928"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['O',\n"," 'B-DOSAGE',\n"," 'B-STRENGTH',\n"," 'I-STRENGTH',\n"," 'B-ROUTE',\n"," 'B-FREQUENCY',\n"," 'I-FREQUENCY',\n"," 'B-DRUG',\n"," 'I-DRUG',\n"," 'B-FORM',\n"," 'I-DOSAGE',\n"," 'B-DURATION',\n"," 'I-DURATION',\n"," 'I-FORM',\n"," 'I-ROUTE']"]},"metadata":{},"execution_count":67}],"source":["posology_ner.getClasses()"]},{"cell_type":"code","execution_count":68,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1712935260304,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"d1IvoqP7wC9b","outputId":"09be36b6-418e-480f-f651-21d03988e8d8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('The', 'O'),\n"," ('patient', 'O'),\n"," ('was', 'O'),\n"," ('prescribed', 'O'),\n"," ('1', 'B-DOSAGE'),\n"," ('capsule', 'B-FORM'),\n"," ('of', 'O'),\n"," ('Advil', 'B-DRUG'),\n"," ('for', 'B-DURATION'),\n"," ('5', 'I-DURATION'),\n"," ('days', 'I-DURATION'),\n"," ('.', 'O'),\n"," ('He', 'O'),\n"," ('was', 'O'),\n"," ('seen', 'O'),\n"," ('by', 'O'),\n"," ('the', 'O'),\n"," ('endocrinology', 'O'),\n"," ('service', 'O'),\n"," ('and', 'O'),\n"," ('she', 'O'),\n"," ('was', 'O'),\n"," ('discharged', 'O'),\n"," ('on', 'O'),\n"," ('40', 'B-DOSAGE'),\n"," ('units', 'I-DOSAGE'),\n"," ('of', 'O'),\n"," ('insulin', 'B-DRUG'),\n"," ('glargine', 'I-DRUG'),\n"," ('at', 'B-FREQUENCY'),\n"," ('night', 'I-FREQUENCY'),\n"," (',', 'O'),\n"," ('12', 'B-DOSAGE'),\n"," ('units', 'I-DOSAGE'),\n"," ('of', 'O'),\n"," ('insulin', 'B-DRUG'),\n"," ('lispro', 'I-DRUG'),\n"," ('with', 'B-FREQUENCY'),\n"," ('meals', 'I-FREQUENCY'),\n"," (',', 'O'),\n"," ('metformin', 'B-DRUG'),\n"," ('1000', 'B-STRENGTH'),\n"," ('mg', 'I-STRENGTH'),\n"," ('two', 'B-FREQUENCY'),\n"," ('times', 'I-FREQUENCY'),\n"," ('a', 'I-FREQUENCY'),\n"," ('day', 'I-FREQUENCY'),\n"," ('.', 'O'),\n"," ('It', 'O'),\n"," ('was', 'O'),\n"," ('determined', 'O'),\n"," ('that', 'O'),\n"," ('all', 'O'),\n"," ('SGLT2', 'B-DRUG'),\n"," ('inhibitors', 'I-DRUG'),\n"," ('should', 'O'),\n"," ('be', 'O'),\n"," ('discontinued', 'O'),\n"," ('indefinitely', 'O'),\n"," ('fro', 'O'),\n"," ('3', 'O'),\n"," ('months', 'O'),\n"," ('.', 'O')]"]},"metadata":{},"execution_count":68}],"source":["light_model = nlp.LightPipeline(ner_chunker_model)\n","\n","text ='The patient was prescribed 1 capsule of Advil for 5 days . He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months .'\n","\n","light_result = light_model.annotate(text)\n","\n","list(zip(light_result['token'], light_result['ner']))"]},{"cell_type":"code","execution_count":69,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1712935260304,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"w3iyF9PVTcUX","outputId":"b6c218e3-aa63-40c7-dfae-b3cf99de9cfd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['insulin glargine at night , 12 units of insulin lispro with meals , metformin 1000 mg two times a day']"]},"metadata":{},"execution_count":69}],"source":["light_result[\"ner_chunk\"]"]},{"cell_type":"markdown","metadata":{"id":"YgSZ4ghNlDbV"},"source":["## Chunk Filterer\n","ChunkFilterer will allow you to filter out named entities by some conditions or predefined look-up lists, so that you can feed these entities to other annotators like Assertion Status or Entity Resolvers. It can be used with two criteria: isin and regex."]},{"cell_type":"code","execution_count":70,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2247,"status":"ok","timestamp":1712935262537,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"mK5A8IozCk-5","outputId":"210670fd-502e-4664-d9c0-b71b0e572aa3"},"outputs":[{"output_type":"stream","name":"stdout","text":["ner_posology download started this may take some time.\n","[OK!]\n"]}],"source":["posology_ner = medical.NerModel.pretrained(\"ner_posology\", \"en\", \"clinical/models\") \\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"]) \\\n","    .setOutputCol(\"ner\")\n","\n","ner_converter = medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\",\"token\",\"ner\"])\\\n","    .setOutputCol(\"ner_chunk\")\n","\n","chunk_filterer = medical.ChunkFilterer()\\\n","    .setInputCols(\"sentence\",\"ner_chunk\")\\\n","    .setOutputCol(\"chunk_filtered\")\\\n","    .setCriteria(\"isin\")\\\n","    .setWhiteList(['ADVIL','Metformin', 'Insulin Lispro'])\\\n","    .setCaseSensitive(False)\n","\n","nlpPipeline = nlp.Pipeline(stages=[\n","    documentAssembler,\n","    sentenceDetector,\n","    tokenizer,\n","    word_embeddings,\n","    posology_ner,\n","    ner_converter,\n","    chunk_filterer])\n","\n","empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n","\n","chunk_filter_model = nlpPipeline.fit(empty_data)"]},{"cell_type":"code","execution_count":71,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1712935262537,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"NnerfAA4C9Oe","outputId":"53c6a7e1-9295-4d45-8eb1-75a55797428b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['document', 'ner_chunk', 'chunk_filtered', 'token', 'ner', 'embeddings', 'sentence'])"]},"metadata":{},"execution_count":71}],"source":["light_model = nlp.LightPipeline(chunk_filter_model)\n","\n","text ='The patient was prescribed 1 capsule of Advil for 5 days . He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months .'\n","\n","light_result = light_model.annotate(text)\n","\n","light_result.keys()"]},{"cell_type":"code","execution_count":72,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712935262537,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"uXGq4DBJDCu7","outputId":"cf702563-4bf3-4ec0-f10b-c14971d9ba4d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['1',\n"," 'capsule',\n"," 'Advil',\n"," 'for 5 days',\n"," '40 units',\n"," 'insulin glargine',\n"," 'at night',\n"," '12 units',\n"," 'insulin lispro',\n"," 'with meals',\n"," 'metformin',\n"," '1000 mg',\n"," 'two times a day',\n"," 'SGLT2 inhibitors']"]},"metadata":{},"execution_count":72}],"source":["light_result['ner_chunk']"]},{"cell_type":"code","execution_count":73,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712935262537,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"Tt-rCxecDeqY","outputId":"61284670-d909-4148-ff9d-4e4169b123ee"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Advil', 'insulin lispro', 'metformin']"]},"metadata":{},"execution_count":73}],"source":["light_result['chunk_filtered']"]},{"cell_type":"code","execution_count":74,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2133,"status":"ok","timestamp":1712935264666,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"3hmPFLKrlZoh","outputId":"1549a4df-926f-451c-c530-99d231d1dc92"},"outputs":[{"output_type":"stream","name":"stdout","text":["ner_clinical download started this may take some time.\n","[OK!]\n"]}],"source":["ner_model = medical.NerModel.pretrained(\"ner_clinical\", \"en\", \"clinical/models\")\\\n","    .setInputCols(\"sentence\",\"token\",\"embeddings\")\\\n","    .setOutputCol(\"ner\")\n","\n","ner_converter = medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\",\"token\",\"ner\"])\\\n","    .setOutputCol(\"ner_chunk\")\n","\n","chunk_filterer = medical.ChunkFilterer()\\\n","    .setInputCols(\"sentence\",\"ner_chunk\")\\\n","    .setOutputCol(\"chunk_filtered\")\\\n","    .setCriteria(\"isin\")\\\n","    .setWhiteList(['severe fever','sore throat'])\\\n","    .setCaseSensitive(False)\n","\n","\n","#   .setCriteria(\"regex\")\\\n","\n","nlpPipeline = nlp.Pipeline(stages=[\n","    documentAssembler,\n","    sentenceDetector,\n","    tokenizer,\n","    word_embeddings,\n","    ner_model,\n","    ner_converter,\n","    chunk_filterer])\n","\n","empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n","\n","chunk_filter_model = nlpPipeline.fit(empty_data)"]},{"cell_type":"code","execution_count":75,"metadata":{"id":"xYkwr0Iwp5KU","executionInfo":{"status":"ok","timestamp":1712935265487,"user_tz":-180,"elapsed":823,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["text = 'Patient with severe fever, severe cough, sore throat, stomach pain, and a headache.'\n","\n","filter_df = spark.createDataFrame([[text]]).toDF(\"text\")\n","\n","chunk_filter_result = chunk_filter_model.transform(filter_df)"]},{"cell_type":"code","execution_count":76,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":670,"status":"ok","timestamp":1712935266155,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"3CTm6zFtApR_","outputId":"daad45b3-bf98-42a1-a2c3-288c581fadee"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-------------------------------------------------------------------+---------------------------+\n","|result                                                             |result                     |\n","+-------------------------------------------------------------------+---------------------------+\n","|[severe fever, severe cough, sore throat, stomach pain, a headache]|[severe fever, sore throat]|\n","+-------------------------------------------------------------------+---------------------------+\n","\n"]}],"source":["chunk_filter_result.select('ner_chunk.result','chunk_filtered.result').show(truncate=False)"]},{"cell_type":"markdown","source":["## ChunkFiltererApproach\n","ChunkFiltererApproach will allow you to enhance filtering by using JSON-Based Entity Confidence Configuration.\n","\n","  + `.setEntitiesConfidenceResourceAsJsonString`: You can   fine tune entity confidence levels using a JSON configuration."],"metadata":{"id":"d8_64ISkufkR"}},{"cell_type":"code","source":["chunk_filterer = medical.ChunkFiltererApproach()\\\n","    .setInputCols(\"sentence\",\"ner_chunk\")\\\n","    .setOutputCol(\"chunk_filtered\")\\\n","    .setFilterEntity(\"entity\")\\\n","    .setEntitiesConfidenceResourceAsJsonString(\"\"\"{'DURATION':'0.9',\n","                                                  'DOSAGE':'0.9',\n","                                                  'FREQUENCY':'0.9',\n","                                                  'STRENGTH':'0.9',\n","                                                  'DRUG':'0.9'}\"\"\")\n","\n","text ='The patient was prescribed 1 capsule of Advil for 5 days . He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months .'"],"metadata":{"id":"IW0BYMkRuo9v","executionInfo":{"status":"ok","timestamp":1712935266155,"user_tz":-180,"elapsed":3,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"execution_count":77,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4WtnMBdJyE1K"},"source":["## Changing entity labels with `NerConverterInternal()`\n","\n","There are two parameters in `NerConverterInternal()` annotator for replacing the labels.\n","\n","+ `.setReplaceLabels`: You can just create a dictionary with the labels and their new values in this parameter.\n","\n","```python\n","jsl_ner_converter_internal = medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\",\"token\",\"jsl_ner\"])\\\n","    .setOutputCol(\"replaced_ner_chunk\")\\\n","    .setReplaceLabels({\"Drug_BrandName\": \"Drug\",\n","                       \"Frequency\": \"Drug_Frequency\",\n","                       \"Dosage\": \"Drug_Dosage\",\n","                       \"Strength\": \"Drug_Strength\"})\n","```\n","\n","+ `.setReplaceDictResource`: You can feed a file that contains the labels and their new values in a dictionary format.\n","\n","```python\n","replace_dict = \"\"\"Drug_BrandName,Drug\n","Frequency,Drug_Frequency\n","Dosage,Drug_Dosage\n","Strength,Drug_Strength\n","\"\"\"\n","with open('replace_dict.csv', 'w') as f:\n","    f.write(replace_dict)\n","\n","...\n","jsl_ner_converter_internal = medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\",\"token\",\"jsl_ner\"])\\\n","    .setOutputCol(\"replaced_ner_chunk\")\\\n","    .setReplaceDictResource(\"replace_dict.csv\",\"text\", {\"delimiter\":\",\"})\n","...\n","```\n","\n","\n","\n","You can check [NerConverterInternal Annotator Page](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators#nerconverterinternal) for more details."]},{"cell_type":"code","execution_count":78,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11113,"status":"ok","timestamp":1712935277265,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"YYMqNMnJyEBk","outputId":"3bc49dbe-91fe-46a6-ef14-0c8b0174b0b4"},"outputs":[{"output_type":"stream","name":"stdout","text":["sentence_detector_dl_healthcare download started this may take some time.\n","Approximate size to download 367.3 KB\n","[OK!]\n","embeddings_clinical download started this may take some time.\n","Approximate size to download 1.6 GB\n","[OK!]\n","ner_jsl download started this may take some time.\n","[OK!]\n"]}],"source":["documentAssembler = nlp.DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","sentenceDetector = nlp.SentenceDetectorDLModel.pretrained(\"sentence_detector_dl_healthcare\",\"en\",\"clinical/models\") \\\n","    .setInputCols([\"document\"]) \\\n","    .setOutputCol(\"sentence\")\n","\n","tokenizer = nlp.Tokenizer()\\\n","    .setInputCols([\"sentence\"])\\\n","    .setOutputCol(\"token\")\n","\n","word_embeddings = nlp.WordEmbeddingsModel.pretrained(\"embeddings_clinical\", \"en\", \"clinical/models\")\\\n","    .setInputCols([\"sentence\", \"token\"])\\\n","    .setOutputCol(\"embeddings\")\n","\n","jsl_ner = medical.NerModel.pretrained(\"ner_jsl\", \"en\", \"clinical/models\") \\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"]) \\\n","    .setOutputCol(\"jsl_ner\")\n","\n","jsl_ner_converter = medical.NerConverterInternal() \\\n","    .setInputCols([\"sentence\", \"token\", \"jsl_ner\"]) \\\n","    .setOutputCol(\"jsl_ner_chunk\")\n","\n","jsl_ner_converter_internal = medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\",\"token\",\"jsl_ner\"])\\\n","    .setOutputCol(\"replaced_ner_chunk\")\\\n","    .setReplaceLabels({\"Drug_BrandName\": \"Drug\",\n","                       \"Frequency\": \"Drug_Frequency\",\n","                       \"Dosage\": \"Drug_Dosage\",\n","                       \"Strength\": \"Drug_Strength\"})\n","\n","nlpPipeline = nlp.Pipeline(stages=[\n","    documentAssembler,\n","    sentenceDetector,\n","    tokenizer,\n","    word_embeddings,\n","    jsl_ner,\n","    jsl_ner_converter,\n","    jsl_ner_converter_internal\n","    ])\n","\n","empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n","ner_converter_model = nlpPipeline.fit(empty_data)"]},{"cell_type":"code","execution_count":79,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":637},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1712935277265,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"JUwrYrviz3J0","outputId":"d022b902-fb55-45c5-b5fb-7c085935e80e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Warning::Spark Session already created, some configs may not take.\n","Warning::Spark Session already created, some configs may not take.\n"]},{"output_type":"execute_result","data":{"text/plain":["  entities_jsl_ner_chunk_origin_sentence entities_jsl_ner_chunk_begin  \\\n","0                                      0                           27   \n","0                                      0                           40   \n","0                                      1                           58   \n","0                                      1                           77   \n","0                                      1                          103   \n","0                                      1                          111   \n","0                                      1                          125   \n","0                                      1                          137   \n","0                                      1                          154   \n","0                                      1                          165   \n","0                                      1                          177   \n","0                                      1                          192   \n","0                                      1                          209   \n","0                                      1                          219   \n","0                                      1                          227   \n","0                                      2                          272   \n","0                                      2                          325   \n","\n","  entities_jsl_ner_chunk_end entities_jsl_ner_chunk  \\\n","0                         35              1 capsule   \n","0                         44                  Parol   \n","0                         59                     He   \n","0                         97  endocrinology service   \n","0                        105                    she   \n","0                        120             discharged   \n","0                        132               40 units   \n","0                        152       insulin glargine   \n","0                        161               at night   \n","0                        172               12 units   \n","0                        190         insulin lispro   \n","0                        201             with meals   \n","0                        217              metformin   \n","0                        225                1000 mg   \n","0                        241        two times a day   \n","0                        287       SGLT2 inhibitors   \n","0                        336           fro 3 months   \n","\n","  entities_jsl_ner_chunk_class entities_replaced_ner_chunk_class  \n","0                       Dosage                       Drug_Dosage  \n","0               Drug_BrandName                              Drug  \n","0                       Gender                            Gender  \n","0                Clinical_Dept                     Clinical_Dept  \n","0                       Gender                            Gender  \n","0          Admission_Discharge               Admission_Discharge  \n","0                       Dosage                       Drug_Dosage  \n","0              Drug_Ingredient                   Drug_Ingredient  \n","0                    Frequency                    Drug_Frequency  \n","0                       Dosage                       Drug_Dosage  \n","0              Drug_Ingredient                   Drug_Ingredient  \n","0                    Frequency                    Drug_Frequency  \n","0              Drug_Ingredient                   Drug_Ingredient  \n","0                     Strength                     Drug_Strength  \n","0                    Frequency                    Drug_Frequency  \n","0              Drug_Ingredient                   Drug_Ingredient  \n","0                     Duration                          Duration  "],"text/html":["\n","  <div id=\"df-721b9151-7dc1-4abe-8bfe-e41284d46e48\" class=\"colab-df-container\">\n","    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<td>Drug_Ingredient</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>192</td>\n","      <td>201</td>\n","      <td>with meals</td>\n","      <td>Frequency</td>\n","      <td>Drug_Frequency</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>209</td>\n","      <td>217</td>\n","      <td>metformin</td>\n","      <td>Drug_Ingredient</td>\n","      <td>Drug_Ingredient</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>219</td>\n","      <td>225</td>\n","      <td>1000 mg</td>\n","      <td>Strength</td>\n","      <td>Drug_Strength</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>227</td>\n","      <td>241</td>\n","      <td>two times a day</td>\n","      <td>Frequency</td>\n","      <td>Drug_Frequency</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","      <td>272</td>\n","      <td>287</td>\n","      <td>SGLT2 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He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely fro 3 months .'\n","\n","#light_model = LightPipeline(ner_converter_model)\n","\n","cols = [\n","     'entities_jsl_ner_chunk_origin_sentence',\n","     'entities_jsl_ner_chunk_begin',\n","     'entities_jsl_ner_chunk_end',\n","     'entities_jsl_ner_chunk',\n","     'entities_jsl_ner_chunk_class',\n","     'entities_replaced_ner_chunk_class'\n","]\n","nlp.nlu.to_pretty_df(ner_converter_model,text,positions=True,output_level='chunk')[cols].head(20)"]},{"cell_type":"markdown","metadata":{"id":"7zkx4ZL2udiS"},"source":["### Adding `setIgnoreStopWords` parameter to control chunk generation.\n","By providing `setIgnoreStopWords`, we can ignore certain words or characters while creating a chunk. <br/>\n","\n","It should be a list of tokens/words or characters, and when two entities of the same type are separated by those words, these entities can be combined to produce a single, larger chunk.\n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"LjXlxH9AwUd6"},"source":["Firstly let's create `NerConverterInternal()` without this parameter"]},{"cell_type":"code","execution_count":80,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9083,"status":"ok","timestamp":1712935286335,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"m9gUWSQBwBzG","outputId":"6a695da4-98c5-473c-e476-587f8e45e359"},"outputs":[{"output_type":"stream","name":"stdout","text":["ner_deid_generic_augmented download started this may take some time.\n","[OK!]\n"]}],"source":["jsl_ner= medical.NerModel.pretrained(\"ner_deid_generic_augmented\", \"en\", \"clinical/models\")\\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"])\\\n","    .setOutputCol(\"ner_deid\")\\\n","    .setLabelCasing('upper')\n","\n","jsl_ner_converter_internal= medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\", \"token\", \"ner_deid\"])\\\n","    .setOutputCol(\"chunk_deid\")\\\n","    .setGreedyMode(True)\\\n","    .setWhiteList(['LOCATION'])\n","\n","nlpPipeline = nlp.Pipeline(stages=[\n","    documentAssembler,\n","    sentenceDetector,\n","    tokenizer,\n","    word_embeddings,\n","    jsl_ner,\n","    jsl_ner_converter_internal\n","    ])\n","\n","empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n","ner_converter_model = nlpPipeline.fit(empty_data)"]},{"cell_type":"code","execution_count":81,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":207},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1712935286335,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"D09_j4bPt_MJ","outputId":"36b93794-d736-4a9a-9f7b-b9f1c81b306b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","<style>\n","    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap');\n","    @import url('https://fonts.googleapis.com/css2?family=Vistol Regular:wght@300;400;500;600;700&display=swap');\n","    \n","    .spark-nlp-display-scroll-entities {\n","        border: 1px solid #E7EDF0;\n","        border-radius: 3px;\n","        text-align: justify;\n","        \n","    }\n","    .spark-nlp-display-scroll-entities span {  \n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #536B76;\n","        font-family: 'Montserrat', sans-serif !important;\n","    }\n","    \n","    .spark-nlp-display-entity-wrapper{\n","    \n","        display: inline-grid;\n","        text-align: center;\n","        border-radius: 4px;\n","        margin: 0 2px 5px 2px;\n","        padding: 1px\n","    }\n","    .spark-nlp-display-entity-name{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        background: #f1f2f3;\n","        border-width: medium;\n","        text-align: center;\n","        \n","        font-weight: 400;\n","        \n","        border-radius: 5px;\n","        padding: 2px 5px;\n","        display: block;\n","        margin: 3px 2px;\n","    \n","    }\n","    .spark-nlp-display-entity-type{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-entity-resolution{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Vistol Regular', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-others{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        font-weight: 400;\n","    }\n","\n","</style>\n"," <span class=\"spark-nlp-display-others\" style=\"background-color: white\"><br>The address of the manufacturer:<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #02C84B\"><span class=\"spark-nlp-display-entity-name\">R K Industry House </span><span class=\"spark-nlp-display-entity-type\">LOCATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #02C84B\"><span class=\"spark-nlp-display-entity-name\">Walbhat Rd </span><span class=\"spark-nlp-display-entity-type\">LOCATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"><br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #02C84B\"><span class=\"spark-nlp-display-entity-name\">Mumbai </span><span class=\"spark-nlp-display-entity-type\">LOCATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #02C84B\"><span class=\"spark-nlp-display-entity-name\">Maharashtra </span><span class=\"spark-nlp-display-entity-type\">LOCATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\">, </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #02C84B\"><span class=\"spark-nlp-display-entity-name\">India </span><span class=\"spark-nlp-display-entity-type\">LOCATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"><br></span></div>"]},"metadata":{}}],"source":["#from sparknlp_display import NerVisualizer\n","\n","text = \"\"\"\n","The address of the manufacturer:\n","R K Industry House, Walbhat Rd\n","Mumbai, Maharashtra, India\n","\"\"\"\n","\n","lmodel= nlp.LightPipeline(ner_converter_model)\n","res = lmodel.fullAnnotate(text)[0]\n","\n","nlp.viz.NerVisualizer().display(res, 'chunk_deid')"]},{"cell_type":"markdown","metadata":{"id":"YX1EApSwx2jn"},"source":["Now, let's define some characters and words with the `setIgnoreStopWords()` parameter and see the difference between the chunks."]},{"cell_type":"code","execution_count":82,"metadata":{"id":"w5hDhPlTxmbS","executionInfo":{"status":"ok","timestamp":1712935288928,"user_tz":-180,"elapsed":2603,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["jsl_ner_converter_internal= medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\", \"token\", \"ner_deid\"])\\\n","    .setOutputCol(\"chunk_deid\")\\\n","    .setGreedyMode(True)\\\n","    .setWhiteList(['LOCATION'])\\\n","    .setIgnoreStopWords(['\\n', ',', \"and\", 'or', '.'])\n","\n","nlpPipeline = nlp.Pipeline(\n","    stages=[\n","        documentAssembler,\n","        sentenceDetector,\n","        tokenizer,\n","        word_embeddings,\n","        jsl_ner,\n","        jsl_ner_converter_internal\n","])\n","\n","empty_data = spark.createDataFrame([[\"\"]]).toDF(\"text\")\n","ner_converter_model = nlpPipeline.fit(empty_data)"]},{"cell_type":"code","execution_count":83,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":207},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712935288928,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"EDMNk__vxmOS","outputId":"dd8dc740-5cec-4b8f-deb2-87d9c47f0508"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","<style>\n","    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap');\n","    @import url('https://fonts.googleapis.com/css2?family=Vistol Regular:wght@300;400;500;600;700&display=swap');\n","    \n","    .spark-nlp-display-scroll-entities {\n","        border: 1px solid #E7EDF0;\n","        border-radius: 3px;\n","        text-align: justify;\n","        \n","    }\n","    .spark-nlp-display-scroll-entities span {  \n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #536B76;\n","        font-family: 'Montserrat', sans-serif !important;\n","    }\n","    \n","    .spark-nlp-display-entity-wrapper{\n","    \n","        display: inline-grid;\n","        text-align: center;\n","        border-radius: 4px;\n","        margin: 0 2px 5px 2px;\n","        padding: 1px\n","    }\n","    .spark-nlp-display-entity-name{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        background: #f1f2f3;\n","        border-width: medium;\n","        text-align: center;\n","        \n","        font-weight: 400;\n","        \n","        border-radius: 5px;\n","        padding: 2px 5px;\n","        display: block;\n","        margin: 3px 2px;\n","    \n","    }\n","    .spark-nlp-display-entity-type{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-entity-resolution{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Vistol Regular', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-others{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        font-weight: 400;\n","    }\n","\n","</style>\n"," <span class=\"spark-nlp-display-others\" style=\"background-color: white\"><br>The address of the manufacturer:<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #017F33\"><span class=\"spark-nlp-display-entity-name\">R K Industry House, Walbhat Rd </span><span class=\"spark-nlp-display-entity-type\">LOCATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"><br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #017F33\"><span class=\"spark-nlp-display-entity-name\">Mumbai, Maharashtra, India </span><span class=\"spark-nlp-display-entity-type\">LOCATION</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"><br></span></div>"]},"metadata":{}}],"source":["lmodel= nlp.LightPipeline(ner_converter_model)\n","res = lmodel.fullAnnotate(text)[0]\n","\n","nlp.viz.NerVisualizer().display(res, 'chunk_deid')"]},{"cell_type":"markdown","metadata":{"id":"c9-Oy0t25tu6"},"source":["## Downloading Pretrained Models\n","\n","- When we use `.pretrained` method, model is downloaded to  a folder named `cache_pretrained` automatically and it is loaded from this folder if you run it again.\n","\n","- In order to download the models manually to any folder, you can use `ResourceDownloader.downloadModelDirectly` from `sparknlp.pretrained` or AWS CLI (steps below). In this case you should use `.load()` method.\n","\n","  - Install AWS CLI to your local computer following the steps [here](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2-linux.html) for Linux and [here](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2-mac.html) for MacOS.\n","\n","  - Then configure your AWS credentials.\n","\n","  - Go to models hub and look for the model you need.\n","\n","  - Select the model you found and you will see the model card that shows all the details about that model.\n","\n","  - Hover the Download button on that page and you will see the download link from the S3 bucket.\n"]},{"cell_type":"code","execution_count":84,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1712935288928,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"4T3_nTI4QZRV","outputId":"30bca97b-e530-4276-c94e-4226d0454e92"},"outputs":[{"output_type":"stream","name":"stdout","text":["cache_pretrained\n"]}],"source":["!cd /root && ls"]},{"cell_type":"code","execution_count":85,"metadata":{"id":"ni6oA3MqP6cM","executionInfo":{"status":"ok","timestamp":1712935290305,"user_tz":-180,"elapsed":1380,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["#The first argument is the path to the zip file and the second one is the folder.\n","nlp.ResourceDownloader.downloadModelDirectly(\"clinical/models/ner_jsl_en_3.1.0_2.4_1624566960534.zip\", \"cache_pretrained\")\n","\n","#or you can use the classic AWS CLI\n","# !aws s3 cp --region us-east-2 s3://auxdata.johnsnowlabs.com/clinical/models/ner_jsl_en_3.1.0_2.4_1624566960534.zip ."]},{"cell_type":"markdown","metadata":{"id":"FcOwTCgB-VnC"},"source":["## Training a Clinical NER (NCBI Disease Dataset)"]},{"cell_type":"markdown","metadata":{"id":"LxOVZe5dgFaW"},"source":["`CoNLL(includeDocId=True).readDataset(spark, \"conll_file.txt\")`  this methos can allow if you have doc_id information in the conll file, you can add this information to the dataframe as a column.\n","\n","```\n","conll=\"\"\"-DOCSTART- -X- -1- O\n","\n","EU NNP B-NP B-ORG\n","rejects VBZ B-VP O\n","\n","-DOCSTART- -X- 2 O\n","\n","Rare NNP B-NP O\n","Hendrix NNP I-NP B-PER\n","\n","-DOCSTART- -X- -3-1- O\n","\n","China NNP B-NP B-LOC\n","says VBZ B-VP O\n","\n","-DOCSTART-\n","\n","China NNP B-NP B-LOC\n","says VBZ B-VP O\n","\"\"\"\n","```\n","```\n","with open('conll_file.txt', 'w') as f:\n","    f.write(conll)\n","\n","data = CoNLL(includeDocId=True).readDataset(spark, \"conll_file.txt\")\n","\n","data.show()\n","```\n","\n","\n","\n","```\n","+------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|doc_id|                text|            document|            sentence|               token|                 pos|               label|\n","+------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|     1|EU rejects German...|[{document, 0, 28...|[{document, 0, 47...|[{token, 0, 1, EU...|[{pos, 0, 1, NNP,...|[{named_entity, 0...|\n","|     2|Rare Hendrix song...|[{document, 0, 97...|[{document, 0, 50...|[{token, 0, 3, Ra...|[{pos, 0, 3, NNP,...|[{named_entity, 0...|\n","|   3-1|China says Taiwan...|[{document, 0, 13...|[{document, 0, 46...|[{token, 0, 4, Ch...|[{pos, 0, 4, NNP,...|[{named_entity, 0...|\n","|     X|China says Taiwan...|[{document, 0, 13...|[{document, 0, 46...|[{token, 0, 4, Ch...|[{pos, 0, 4, NNP,...|[{named_entity, 0...|\n","+------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","```"]},{"cell_type":"code","execution_count":86,"metadata":{"id":"vxPi21kH-Zbj","executionInfo":{"status":"ok","timestamp":1712935291675,"user_tz":-180,"elapsed":1372,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Certification_Trainings/Healthcare/data/NER_NCBIconlltrain.txt\n","!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Certification_Trainings/Healthcare/data/NER_NCBIconlltest.txt"]},{"cell_type":"code","execution_count":87,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2621,"status":"ok","timestamp":1712935294295,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"thHY_1pi-cLu","outputId":"e503e557-3d04-4745-813e-549e822ae88d"},"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|                text|            document|            sentence|               token|                 pos|               label|\n","+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|Identification of...|[{document, 0, 89...|[{document, 0, 89...|[{token, 0, 13, I...|[{pos, 0, 13, NN,...|[{named_entity, 0...|\n","|The adenomatous p...|[{document, 0, 21...|[{document, 0, 21...|[{token, 0, 2, Th...|[{pos, 0, 2, NN, ...|[{named_entity, 0...|\n","|Complex formation...|[{document, 0, 63...|[{document, 0, 63...|[{token, 0, 6, Co...|[{pos, 0, 6, NN, ...|[{named_entity, 0...|\n","+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","only showing top 3 rows\n","\n"]}],"source":["conll_data = nlp.CoNLL().readDataset(spark, 'NER_NCBIconlltrain.txt')\n","\n","conll_data.show(3)"]},{"cell_type":"code","execution_count":88,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1184,"status":"ok","timestamp":1712935295476,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"83cbUZVM-9L1","outputId":"05090553-8912-4419-b020-2261e5094114"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["3266"]},"metadata":{},"execution_count":88}],"source":["conll_data.count()"]},{"cell_type":"code","execution_count":89,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1290,"status":"ok","timestamp":1712935296764,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"nh5enx4p-spM","outputId":"7d4d6410-a70e-483c-d913-8368864391a1"},"outputs":[{"output_type":"stream","name":"stdout","text":["+------------+-----+\n","|ground_truth|count|\n","+------------+-----+\n","|O           |75093|\n","|I-Disease   |3547 |\n","|B-Disease   |3093 |\n","+------------+-----+\n","\n"]}],"source":["\n","conll_data.select(F.explode(F.arrays_zip(conll_data.token.result,\n","                                         conll_data.label.result)).alias(\"cols\")) \\\n","          .select(F.expr(\"cols['0']\").alias(\"token\"),\n","                  F.expr(\"cols['1']\").alias(\"ground_truth\"))\\\n","          .groupBy('ground_truth')\\\n","          .count()\\\n","          .orderBy('count', ascending=False)\\\n","          .show(100,truncate=False)"]},{"cell_type":"code","execution_count":90,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1151,"status":"ok","timestamp":1712935297913,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"48XUc9WX_fjs","outputId":"af783abf-9af8-40f5-e7a5-ed51cfae3b0b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["1537"]},"metadata":{},"execution_count":90}],"source":["conll_data.select(\"label.result\").distinct().count()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Rkg9IcR--_PF"},"outputs":[],"source":["'''\n","As you can see, there are too many `O` labels in the dataset.\n","To make it more balanced, we can drop the sentences have only O labels.\n","(`c>1` means we drop all the sentences that have no valuable labels other than `O`)\n","'''\n","\n","'''\n","conll_data = conll_data.withColumn('unique', F.array_distinct(\"label.result\"))\\\n","                       .withColumn('c', F.size('unique'))\\\n","                       .filter(F.col('c')>1)\n","\n","conll_data.select(F.explode(F.arrays_zip(conll_data.token.result,conll_data.label.result)).alias(\"cols\")) \\\n","          .select(F.expr(\"cols['0']\").alias(\"token\"),\n","                  F.expr(\"cols['1']\").alias(\"ground_truth\"))\\\n","          .groupBy('ground_truth')\\\n","          .count()\\\n","          .orderBy('count', ascending=False)\\\n","          .show(100,truncate=False)\n","'''"]},{"cell_type":"code","execution_count":92,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4085,"status":"ok","timestamp":1712935301994,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"mTopTnOJAV51","outputId":"73203ea6-aa14-46c1-a082-1c30503e4708"},"outputs":[{"output_type":"stream","name":"stdout","text":["embeddings_clinical download started this may take some time.\n","Approximate size to download 1.6 GB\n","[OK!]\n"]}],"source":["# Clinical word embeddings trained on PubMED dataset\n","clinical_embeddings = nlp.WordEmbeddingsModel.pretrained('embeddings_clinical', \"en\", \"clinical/models\")\\\n","    .setInputCols([\"sentence\", \"token\"])\\\n","    .setOutputCol(\"embeddings\")"]},{"cell_type":"code","execution_count":93,"metadata":{"id":"lRNBxjqkQO1U","executionInfo":{"status":"ok","timestamp":1712935305322,"user_tz":-180,"elapsed":3337,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["test_data = nlp.CoNLL().readDataset(spark, 'NER_NCBIconlltest.txt')\n","\n","test_data = clinical_embeddings.transform(test_data)\n","\n","test_data.write.mode(\"overwrite\").parquet('NER_NCBIconlltest.parquet')"]},{"cell_type":"markdown","metadata":{"id":"dPNI5AQ4W4oE"},"source":["### NERDL Graph\n"]},{"cell_type":"markdown","metadata":{"id":"XA2JSVK-wDBy"},"source":["We will use `TFGraphBuilder` annotator which can be used to create graphs in the model training pipeline. `TFGraphBuilder` inspects the data and creates the proper graph if a suitable version of TensorFlow is available. The graph is stored in the defined folder and loaded by the `MedicalNerApproach` annotator."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9JaZJg06hLM0"},"outputs":[],"source":["!pip install -q tensorflow==2.12.0\n","!pip install -q tensorflow-addons"]},{"cell_type":"code","execution_count":95,"metadata":{"id":"wSQ4X33bwZL1","executionInfo":{"status":"ok","timestamp":1712935379447,"user_tz":-180,"elapsed":9,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["graph_folder_path = \"medical_ner_graphs\"\n","\n","ner_graph_builder = medical.TFGraphBuilder()\\\n","    .setModelName(\"ner_dl\")\\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"]) \\\n","    .setLabelColumn(\"label\")\\\n","    .setGraphFolder(graph_folder_path)\\\n","    .setGraphFile(\"auto\")\\\n","    .setHiddenUnitsNumber(20)\\\n","    .setIsLicensed(True) # False -> if you want to use TFGraphBuilder with NerDLApproach"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"znd3vD3_0y8L"},"outputs":[],"source":["# TensorFlow graph file (`.pb` extension) can be produced for NER training externally.\n","# If this method is used, graph folder should be added to MedicalNerApproach training\n","# model as `.setGraphFolder(graph_folder_path)` .\n","\n","'''\n","medical.tf_graph.print_model_params(\"ner_dl\")\n","\n","graph_folder_path = \"medical_ner_graphs\"\n","\n","medical.tf_graph.build(\"ner_dl\",\n","               build_params={\"embeddings_dim\": 200,\n","                             \"nchars\": 85,\n","                             \"ntags\": 3,\n","                             \"is_medical\": 1},\n","               model_location=graph_folder_path,\n","               model_filename=\"auto\")\n","'''\n","\n","# for open source users\n","\n","'''\n","!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/training/english/dl-ner/nerdl-graph/create_graph.py\n","!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/training/english/dl-ner/nerdl-graph/dataset_encoder.py\n","!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/training/english/dl-ner/nerdl-graph/ner_model.py\n","!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/training/english/dl-ner/nerdl-graph/ner_model_saver.py\n","!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/training/english/dl-ner/nerdl-graph/sentence_grouper.py\n","\n","!pip -q install tensorflow==1.15.0\n","\n","import create_graph\n","\n","ntags = 3 # number of labels\n","embeddings_dim = 200\n","nchars =83\n","\n","create_graph.create_graph(ntags, embeddings_dim, nchars)\n","'''"]},{"cell_type":"markdown","metadata":{"id":"Vd8mR5eAYwc7"},"source":["We will use `setEarlyStoppingCriterion` and `setEarlyStoppingPatience` parameters to stop training if it doesn't improve any more. Also, these parameters will provide us to save time and resources.\n","\n","- `setEarlyStoppingCriterion(float)`: This parameter is used to set the minimal improvement of the test metric to terminate training. The metric monitored is the same which is used to save the best best model (macro F1 if using test or validation set, loss otherwise). If it is F1, then training is stopped if `F1_current - F1_previous < earlyStoppingCriterion`. If monitoring loss, then training is stopped if `loss_previous - loss_current < earlyStoppingCriterion`. If earlyStoppingCriterion is `0`, then no early stopping is applied. Default is `0`.\n","\n","- `setEarlyStoppingPatience(int)`: This is the number of epoch without improvement which will be tolerated. Default is `0`, which means that early stopping will occur at the first time when performance in the current epoch is no better than in the previous epoch (taking earlyStoppingCriterion into account)."]},{"cell_type":"code","execution_count":98,"metadata":{"id":"WzVgfInoJS3I","executionInfo":{"status":"ok","timestamp":1712935379448,"user_tz":-180,"elapsed":8,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["nerTagger = medical.NerApproach()\\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"])\\\n","    .setLabelColumn(\"label\")\\\n","    .setOutputCol(\"ner\")\\\n","    .setMaxEpochs(30)\\\n","    .setBatchSize(64)\\\n","    .setRandomSeed(0)\\\n","    .setVerbose(1)\\\n","    .setValidationSplit(0.2)\\\n","    .setEvaluationLogExtended(True) \\\n","    .setEnableOutputLogs(True)\\\n","    .setIncludeConfidence(True)\\\n","    .setOutputLogsPath('ner_logs')\\\n","    .setTestDataset(\"NER_NCBIconlltest.parquet\")\\\n","    .setUseBestModel(True)\\\n","    .setEarlyStoppingCriterion(0.04)\\\n","    .setEarlyStoppingPatience(3)\\\n","    .setGraphFolder(graph_folder_path)\n","   # .setEnableMemoryOptimizer(True) #>> if you have a limited memory and a large conll file, you can set this True to train batch by batch\n","\n","ner_pipeline = nlp.Pipeline(stages=[\n","          clinical_embeddings,\n","          ner_graph_builder,\n","          nerTagger\n"," ])"]},{"cell_type":"markdown","metadata":{"id":"goG7cYUi2_09"},"source":["You can visit [1.4.Resume_MedicalNer_Model_Training.ipynb](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.4.Resume_MedicalNer_Model_Training.ipynb) notebook for fine-tuning pretrained NER models and more details of `MedicalNerApproach()` parameters."]},{"cell_type":"code","execution_count":99,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":234693,"status":"ok","timestamp":1712935614133,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"aiXOjTzpBdrQ","outputId":"23965c7f-378b-4ca8-c7dc-825d52e9aca8"},"outputs":[{"output_type":"stream","name":"stdout","text":["TF Graph Builder configuration:\n","Model name: ner_dl\n","Graph folder: medical_ner_graphs\n","Graph file name: auto\n","Build params: {'ntags': 3, 'embeddings_dim': 200, 'nchars': 85, 'is_medical': True, 'lstm_size': 20}\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","non-resource variables are not supported in the long term\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/init_ops.py:93: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Call initializer instance with the dtype argument instead of passing it to the constructor\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/init_ops.py:93: calling Orthogonal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Call initializer instance with the dtype argument instead of passing it to the constructor\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/init_ops.py:93: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Call initializer instance with the dtype argument instead of passing it to the constructor\n"]},{"output_type":"stream","name":"stdout","text":["ner_dl graph exported to medical_ner_graphs/blstm_3_200_20_85.pb\n","CPU times: user 15.1 s, sys: 614 ms, total: 15.7 s\n","Wall time: 3min 54s\n"]}],"source":["%%time\n","ner_model = ner_pipeline.fit(conll_data)"]},{"cell_type":"markdown","metadata":{"id":"y-SOgKz4yow0"},"source":["If you get an error for incompatible TF graph, use [4.1 NerDL-Graph](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings_JSL/Public/4.1_NerDL_Graph.ipynb) notebook in public folder to create a graph\n","\n","Licensed users can also use [17.Graph_builder_for_DL_models.ipynb](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/17.Graph_builder_for_DL_models.ipynb) to create TF graphs easily."]},{"cell_type":"markdown","metadata":{"id":"Huet0xVDzRee"},"source":["\n","`getTrainingClassDistribution()` parameter returns the distribution of labels used when training the NER model."]},{"cell_type":"code","execution_count":100,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1712935614133,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"CbWpSBNBLxx2","outputId":"d0aa48a0-7bde-4fe7-f9bc-436020c0d967"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'O': 75093, 'B-Disease': 3093, 'I-Disease': 3547}"]},"metadata":{},"execution_count":100}],"source":["ner_model.stages[2].getTrainingClassDistribution()"]},{"cell_type":"markdown","metadata":{"id":"wG1Md0T-u7He"},"source":["Let's check the results saved in the log file."]},{"cell_type":"code","execution_count":101,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1712935614133,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"TtrxZvAwtZIo","outputId":"58a5486a-122e-421e-aa79-3fb4441a3e7e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Name of the selected graph: /content/medical_ner_graphs/blstm_3_200_20_85.pb\n","Training started - total epochs: 30 - lr: 0.001 - batch size: 64 - labels: 3 - chars: 84 - training examples: 3266\n","\n","\n","Epoch 1/30 started, lr: 0.001, dataset size: 3266\n","\n","\n","Epoch 1/30 - 14.82s - loss: 637.2786 - avg training loss: 14.820434 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.61s\n","Total validation loss: 100.9458\tAvg validation loss: 7.2104\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 1\t 0\t 697\t 1.0\t 0.0014326648\t 0.0028612304\n","B-Disease\t 0\t 0\t 663\t 0.0\t 0.0\t 0.0\n","tp: 1 fp: 0 fn: 1360 labels: 2\n","Macro-average\t prec: 0.5, rec: 7.163324E-4, f1: 0.0014306152\n","Micro-average\t prec: 1.0, rec: 7.3475385E-4, f1: 0.0014684286\n","Quality on test dataset: \n","time to finish evaluation: 1.01s\n","Total test loss: 105.8674\tAvg test loss: 7.5620\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 0\t 0\t 789\t 0.0\t 0.0\t 0.0\n","B-Disease\t 0\t 0\t 708\t 0.0\t 0.0\t 0.0\n","tp: 0 fp: 0 fn: 1497 labels: 2\n","Macro-average\t prec: 0.0, rec: 0.0, f1: NaN\n","Micro-average\t prec: 0.0, rec: 0.0, f1: 0.0\n","\n","\n","Epoch 2/30 started, lr: 9.950249E-4, dataset size: 3266\n","\n","\n","Epoch 2/30 - 11.07s - loss: 344.18954 - avg training loss: 8.004408 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.06s\n","Total validation loss: 77.0325\tAvg validation loss: 5.5023\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 33\t 6\t 665\t 0.84615386\t 0.04727794\t 0.089552246\n","B-Disease\t 37\t 10\t 626\t 0.78723407\t 0.05580694\t 0.10422535\n","tp: 70 fp: 16 fn: 1291 labels: 2\n","Macro-average\t prec: 0.81669396, rec: 0.05154244, f1: 0.09696529\n","Micro-average\t prec: 0.81395346, rec: 0.05143277, f1: 0.0967519\n","Quality on test dataset: \n","time to finish evaluation: 1.01s\n","Total test loss: 79.9457\tAvg test loss: 5.7104\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 33\t 3\t 756\t 0.9166667\t 0.041825093\t 0.08\n","B-Disease\t 32\t 16\t 676\t 0.6666667\t 0.04519774\t 0.08465609\n","tp: 65 fp: 19 fn: 1432 labels: 2\n","Macro-average\t prec: 0.7916667, rec: 0.043511417, f1: 0.08248909\n","Micro-average\t prec: 0.77380955, rec: 0.043420173, f1: 0.08222643\n","\n","\n","Epoch 3/30 started, lr: 9.90099E-4, dataset size: 3266\n","\n","\n","Epoch 3/30 - 16.58s - loss: 256.67395 - avg training loss: 5.9691615 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.11s\n","Total validation loss: 55.7582\tAvg validation loss: 3.9827\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 232\t 84\t 466\t 0.73417723\t 0.3323782\t 0.45759365\n","B-Disease\t 214\t 58\t 449\t 0.7867647\t 0.32277527\t 0.457754\n","tp: 446 fp: 142 fn: 915 labels: 2\n","Macro-average\t prec: 0.760471, rec: 0.32757676, f1: 0.45790753\n","Micro-average\t prec: 0.7585034, rec: 0.32770023, f1: 0.4576706\n","Quality on test dataset: \n","time to finish evaluation: 1.03s\n","Total test loss: 59.0398\tAvg test loss: 4.2171\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 248\t 95\t 541\t 0.72303206\t 0.31432194\t 0.43816257\n","B-Disease\t 231\t 68\t 477\t 0.77257526\t 0.32627118\t 0.45878845\n","tp: 479 fp: 163 fn: 1018 labels: 2\n","Macro-average\t prec: 0.7478037, rec: 0.32029656, f1: 0.44849527\n","Micro-average\t prec: 0.7461059, rec: 0.3199733, f1: 0.44787288\n","\n","\n","Epoch 4/30 started, lr: 9.852217E-4, dataset size: 3266\n","\n","\n","Epoch 4/30 - 11.71s - loss: 200.46725 - avg training loss: 4.6620293 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.03s\n","Total validation loss: 47.3427\tAvg validation loss: 3.3816\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 319\t 118\t 379\t 0.72997713\t 0.45702004\t 0.56211454\n","B-Disease\t 301\t 85\t 362\t 0.7797927\t 0.453997\t 0.5738799\n","tp: 620 fp: 203 fn: 741 labels: 2\n","Macro-average\t prec: 0.75488496, rec: 0.45550853, f1: 0.56817317\n","Micro-average\t prec: 0.75334144, rec: 0.4555474, f1: 0.5677656\n","Quality on test dataset: \n","time to finish evaluation: 1.02s\n","Total test loss: 51.2355\tAvg test loss: 3.6597\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 359\t 138\t 430\t 0.722334\t 0.45500633\t 0.5583204\n","B-Disease\t 316\t 90\t 392\t 0.77832514\t 0.4463277\t 0.567325\n","tp: 675 fp: 228 fn: 822 labels: 2\n","Macro-average\t prec: 0.7503296, rec: 0.45066702, f1: 0.56311363\n","Micro-average\t prec: 0.7475083, rec: 0.4509018, f1: 0.56250006\n","\n","\n","Epoch 5/30 started, lr: 9.803922E-4, dataset size: 3266\n","\n","\n","Epoch 5/30 - 10.82s - loss: 180.083 - avg training loss: 4.1879764 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.02s\n","Total validation loss: 41.7652\tAvg validation loss: 2.9832\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 410\t 149\t 288\t 0.7334526\t 0.58739257\t 0.6523469\n","B-Disease\t 398\t 109\t 265\t 0.78500986\t 0.6003017\t 0.68034184\n","tp: 808 fp: 258 fn: 553 labels: 2\n","Macro-average\t prec: 0.7592312, rec: 0.59384716, f1: 0.6664319\n","Micro-average\t prec: 0.75797373, rec: 0.5936811, f1: 0.66584265\n","Quality on test dataset: \n","time to finish evaluation: 1.00s\n","Total test loss: 43.6450\tAvg test loss: 3.1175\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 460\t 174\t 329\t 0.725552\t 0.58301646\t 0.6465214\n","B-Disease\t 414\t 117\t 294\t 0.779661\t 0.58474576\t 0.66828084\n","tp: 874 fp: 291 fn: 623 labels: 2\n","Macro-average\t prec: 0.7526065, rec: 0.58388114, f1: 0.6575934\n","Micro-average\t prec: 0.7502146, rec: 0.58383435, f1: 0.6566491\n","\n","\n","Epoch 6/30 started, lr: 9.756098E-4, dataset size: 3266\n","\n","\n","Epoch 6/30 - 10.61s - loss: 164.66263 - avg training loss: 3.8293633 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.04s\n","Total validation loss: 38.6400\tAvg validation loss: 2.7600\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 350\t 53\t 348\t 0.86848634\t 0.50143266\t 0.63578564\n","B-Disease\t 408\t 107\t 255\t 0.792233\t 0.61538464\t 0.69269955\n","tp: 758 fp: 160 fn: 603 labels: 2\n","Macro-average\t prec: 0.8303597, rec: 0.5584086, f1: 0.66775715\n","Micro-average\t prec: 0.82570803, rec: 0.5569434, f1: 0.66520405\n","Quality on test dataset: \n","time to finish evaluation: 1.00s\n","Total test loss: 45.2877\tAvg test loss: 3.2348\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 387\t 71\t 402\t 0.84497815\t 0.4904943\t 0.62068963\n","B-Disease\t 431\t 127\t 277\t 0.77240145\t 0.6087571\t 0.68088466\n","tp: 818 fp: 198 fn: 679 labels: 2\n","Macro-average\t prec: 0.80868983, rec: 0.5496257, f1: 0.65445286\n","Micro-average\t prec: 0.8051181, rec: 0.5464262, f1: 0.6510147\n","\n","\n","Epoch 7/30 started, lr: 9.7087387E-4, dataset size: 3266\n","\n","\n","Epoch 7/30 - 11.49s - loss: 155.29387 - avg training loss: 3.6114852 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.07s\n","Total validation loss: 35.6490\tAvg validation loss: 2.5464\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 476\t 191\t 222\t 0.7136432\t 0.6819484\t 0.6974359\n","B-Disease\t 451\t 124\t 212\t 0.78434783\t 0.68024135\t 0.7285946\n","tp: 927 fp: 315 fn: 434 labels: 2\n","Macro-average\t prec: 0.74899554, rec: 0.6810949, f1: 0.71343327\n","Micro-average\t prec: 0.7463768, rec: 0.6811168, f1: 0.7122551\n","Quality on test dataset: \n","time to finish evaluation: 1.00s\n","Total test loss: 39.6128\tAvg test loss: 2.8295\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 525\t 217\t 264\t 0.7075472\t 0.66539925\t 0.68582624\n","B-Disease\t 471\t 124\t 237\t 0.79159665\t 0.66525424\t 0.722947\n","tp: 996 fp: 341 fn: 501 labels: 2\n","Macro-average\t prec: 0.7495719, rec: 0.6653267, f1: 0.7049413\n","Micro-average\t prec: 0.74495137, rec: 0.66533065, f1: 0.70289344\n","\n","\n","Epoch 8/30 started, lr: 9.6618367E-4, dataset size: 3266\n","\n","\n","Epoch 8/30 - 11.49s - loss: 142.10468 - avg training loss: 3.30476 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.06s\n","Total validation loss: 36.0479\tAvg validation loss: 2.5749\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 392\t 55\t 306\t 0.8769575\t 0.56160456\t 0.6847161\n","B-Disease\t 437\t 109\t 226\t 0.8003663\t 0.6591252\t 0.7229115\n","tp: 829 fp: 164 fn: 532 labels: 2\n","Macro-average\t prec: 0.8386619, rec: 0.6103649, f1: 0.7065291\n","Micro-average\t prec: 0.83484393, rec: 0.60911095, f1: 0.704333\n","Quality on test dataset: \n","time to finish evaluation: 1.00s\n","Total test loss: 38.7947\tAvg test loss: 2.7711\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 433\t 71\t 356\t 0.859127\t 0.54879594\t 0.6697602\n","B-Disease\t 451\t 117\t 257\t 0.7940141\t 0.6370056\t 0.7068965\n","tp: 884 fp: 188 fn: 613 labels: 2\n","Macro-average\t prec: 0.8265705, rec: 0.59290075, f1: 0.6905026\n","Micro-average\t prec: 0.82462686, rec: 0.59051436, f1: 0.68820554\n","\n","\n","Epoch 9/30 started, lr: 9.615385E-4, dataset size: 3266\n","\n","\n","Epoch 9/30 - 12.18s - loss: 128.1541 - avg training loss: 2.9803278 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.05s\n","Total validation loss: 35.1161\tAvg validation loss: 2.5083\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 384\t 49\t 314\t 0.88683605\t 0.55014324\t 0.6790451\n","B-Disease\t 429\t 93\t 234\t 0.8218391\t 0.64705884\t 0.7240507\n","tp: 813 fp: 142 fn: 548 labels: 2\n","Macro-average\t prec: 0.8543376, rec: 0.59860104, f1: 0.70396286\n","Micro-average\t prec: 0.8513089, rec: 0.5973549, f1: 0.70207256\n","Quality on test dataset: \n","time to finish evaluation: 1.10s\n","Total test loss: 41.4514\tAvg test loss: 2.9608\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 424\t 68\t 365\t 0.86178863\t 0.5373891\t 0.6619828\n","B-Disease\t 441\t 107\t 267\t 0.80474454\t 0.62288135\t 0.70222926\n","tp: 865 fp: 175 fn: 632 labels: 2\n","Macro-average\t prec: 0.8332666, rec: 0.5801352, f1: 0.6840338\n","Micro-average\t prec: 0.8317308, rec: 0.5778223, f1: 0.6819078\n","\n","\n","Epoch 10/30 started, lr: 9.569379E-4, dataset size: 3266\n","\n","\n","Epoch 10/30 - 12.31s - loss: 126.72783 - avg training loss: 2.9471588 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.11s\n","Total validation loss: 29.9304\tAvg validation loss: 2.1379\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 452\t 78\t 246\t 0.8528302\t 0.6475645\t 0.7361564\n","B-Disease\t 496\t 135\t 167\t 0.7860539\t 0.74811465\t 0.76661515\n","tp: 948 fp: 213 fn: 413 labels: 2\n","Macro-average\t prec: 0.81944203, rec: 0.69783956, f1: 0.75376797\n","Micro-average\t prec: 0.81653744, rec: 0.6965467, f1: 0.7517843\n","Quality on test dataset: \n","time to finish evaluation: 1.00s\n","Total test loss: 32.4125\tAvg test loss: 2.3152\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 510\t 93\t 279\t 0.84577113\t 0.6463878\t 0.7327586\n","B-Disease\t 536\t 137\t 172\t 0.79643387\t 0.75706214\t 0.77624905\n","tp: 1046 fp: 230 fn: 451 labels: 2\n","Macro-average\t prec: 0.8211025, rec: 0.701725, f1: 0.7567346\n","Micro-average\t prec: 0.81974924, rec: 0.69873077, f1: 0.75441754\n","\n","\n","Epoch 11/30 started, lr: 9.5238106E-4, dataset size: 3266\n","\n","\n","Epoch 11/30 - 12.08s - loss: 123.620995 - avg training loss: 2.8749068 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.05s\n","Total validation loss: 29.4633\tAvg validation loss: 2.1045\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 550\t 190\t 148\t 0.7432432\t 0.7879656\t 0.76495135\n","B-Disease\t 504\t 134\t 159\t 0.78996867\t 0.760181\t 0.7747886\n","tp: 1054 fp: 324 fn: 307 labels: 2\n","Macro-average\t prec: 0.766606, rec: 0.7740733, f1: 0.7703216\n","Micro-average\t prec: 0.7648766, rec: 0.7744306, f1: 0.76962394\n","Quality on test dataset: \n","time to finish evaluation: 1.02s\n","Total test loss: 31.9479\tAvg test loss: 2.2820\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 590\t 228\t 199\t 0.7212714\t 0.747782\t 0.7342875\n","B-Disease\t 537\t 135\t 171\t 0.79910713\t 0.7584746\t 0.77826095\n","tp: 1127 fp: 363 fn: 370 labels: 2\n","Macro-average\t prec: 0.7601893, rec: 0.7531283, f1: 0.75664234\n","Micro-average\t prec: 0.75637585, rec: 0.752839, f1: 0.7546033\n","\n","\n","Epoch 12/30 started, lr: 9.478674E-4, dataset size: 3266\n","\n","\n","Epoch 12/30 - 11.13s - loss: 114.46572 - avg training loss: 2.6619935 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.18s\n","Total validation loss: 30.4022\tAvg validation loss: 2.1716\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 452\t 64\t 246\t 0.875969\t 0.6475645\t 0.7446458\n","B-Disease\t 469\t 98\t 194\t 0.8271605\t 0.70739067\t 0.7626017\n","tp: 921 fp: 162 fn: 440 labels: 2\n","Macro-average\t prec: 0.85156476, rec: 0.6774776, f1: 0.75461096\n","Micro-average\t prec: 0.8504155, rec: 0.6767083, f1: 0.75368243\n","Quality on test dataset: \n","time to finish evaluation: 1.07s\n","Total test loss: 31.4122\tAvg test loss: 2.2437\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 505\t 81\t 284\t 0.86177474\t 0.6400507\t 0.73454547\n","B-Disease\t 493\t 104\t 215\t 0.82579565\t 0.6963277\t 0.75555557\n","tp: 998 fp: 185 fn: 499 labels: 2\n","Macro-average\t prec: 0.84378517, rec: 0.66818917, f1: 0.74579054\n","Micro-average\t prec: 0.8436179, rec: 0.6666667, f1: 0.7447761\n","\n","\n","Epoch 13/30 started, lr: 9.433963E-4, dataset size: 3266\n","\n","\n","Epoch 13/30 - 11.46s - loss: 105.74969 - avg training loss: 2.459295 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.03s\n","Total validation loss: 26.0859\tAvg validation loss: 1.8633\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 446\t 54\t 252\t 0.892\t 0.63896847\t 0.7445743\n","B-Disease\t 493\t 125\t 170\t 0.7977346\t 0.74358976\t 0.76971114\n","tp: 939 fp: 179 fn: 422 labels: 2\n","Macro-average\t prec: 0.84486735, rec: 0.6912791, f1: 0.76039517\n","Micro-average\t prec: 0.8398927, rec: 0.6899339, f1: 0.75756353\n","Quality on test dataset: \n","time to finish evaluation: 1.00s\n","Total test loss: 30.6030\tAvg test loss: 2.1859\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 508\t 70\t 281\t 0.8788927\t 0.64385295\t 0.7432334\n","B-Disease\t 539\t 124\t 169\t 0.81297135\t 0.76129943\t 0.78628737\n","tp: 1047 fp: 194 fn: 450 labels: 2\n","Macro-average\t prec: 0.845932, rec: 0.70257616, f1: 0.7676184\n","Micro-average\t prec: 0.8436745, rec: 0.6993988, f1: 0.76479185\n","\n","\n","Epoch 14/30 started, lr: 9.389671E-4, dataset size: 3266\n","\n","\n","Epoch 14/30 - 11.48s - loss: 101.847885 - avg training loss: 2.3685555 - batches: 43\n","Quality on validation dataset (20.0%), validation examples = 653\n","time to finish evaluation: 1.05s\n","Total validation loss: 25.2069\tAvg validation loss: 1.8005\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 470\t 57\t 228\t 0.89184064\t 0.6733524\t 0.7673469\n","B-Disease\t 497\t 108\t 166\t 0.8214876\t 0.74962294\t 0.78391176\n","tp: 967 fp: 165 fn: 394 labels: 2\n","Macro-average\t prec: 0.8566641, rec: 0.71148765, f1: 0.77735585\n","Micro-average\t prec: 0.8542403, rec: 0.710507, f1: 0.7757721\n","Quality on test dataset: \n","time to finish evaluation: 0.98s\n","Total test loss: 29.0728\tAvg test loss: 2.0766\n","label\t tp\t fp\t fn\t prec\t rec\t f1\n","I-Disease\t 524\t 70\t 265\t 0.8821549\t 0.6641318\t 0.7577729\n","B-Disease\t 538\t 114\t 170\t 0.82515335\t 0.759887\t 0.79117644\n","tp: 1062 fp: 184 fn: 435 labels: 2\n","Macro-average\t prec: 0.85365415, rec: 0.71200943, f1: 0.7764245\n","Micro-average\t prec: 0.85232747, rec: 0.70941883, f1: 0.77433467\n","Early stopping: test set macro F1 has not improved for more than 3 epochs\n","\n"]}],"source":["import os\n","log_file= os.listdir(\"ner_logs\")[0]\n","\n","with open (f\"./ner_logs/{log_file}\") as f:\n","  print(f.read())"]},{"cell_type":"markdown","metadata":{"id":"0_rSimOoUa8v"},"source":["As you see above, our **earlyStopping** feature worked, trainining was terminated before 30th epoch."]},{"cell_type":"markdown","metadata":{"id":"rUehS3qTdHUh"},"source":["### Evaluate your model"]},{"cell_type":"code","execution_count":102,"metadata":{"id":"5GV4YaeHcsHB","executionInfo":{"status":"ok","timestamp":1712935614134,"user_tz":-180,"elapsed":15,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["pred_df = ner_model.stages[2].transform(test_data)"]},{"cell_type":"code","execution_count":103,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1712935614134,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"tICjE0Yqc2Ri","outputId":"c84aaf24-fe80-4657-a04f-f7c80619d7bb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['text', 'document', 'sentence', 'token', 'pos', 'label', 'embeddings', 'ner']"]},"metadata":{},"execution_count":103}],"source":["pred_df.columns"]},{"cell_type":"code","execution_count":104,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8751,"status":"ok","timestamp":1712935622879,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"B9Qc_oukcj94","outputId":"a3a57e0d-9517-4560-9e4c-03573e1192eb"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-------+-----+-----+-----+-----+---------+------+------+\n","| entity|   tp|   fp|   fn|total|precision|recall|    f1|\n","+-------+-----+-----+-----+-----+---------+------+------+\n","|Disease|515.0|187.0|189.0|704.0|   0.7336|0.7315|0.7326|\n","+-------+-----+-----+-----+-----+---------+------+------+\n","\n","+-----------------+\n","|            macro|\n","+-----------------+\n","|0.732574679943101|\n","+-----------------+\n","\n","None\n","+-----------------+\n","|            micro|\n","+-----------------+\n","|0.732574679943101|\n","+-----------------+\n","\n","None\n"]}],"source":["evaler = medical.NerDLMetrics(mode=\"full_chunk\")\n","\n","eval_result = evaler.computeMetricsFromDF(pred_df.select(\"label\",\"ner\"), prediction_col=\"ner\", label_col=\"label\", drop_o = True, case_sensitive = True).cache()\n","\n","eval_result.withColumn(\"precision\", F.round(eval_result[\"precision\"],4))\\\n","           .withColumn(\"recall\", F.round(eval_result[\"recall\"],4))\\\n","           .withColumn(\"f1\", F.round(eval_result[\"f1\"],4)).show(100)\n","\n","print(eval_result.selectExpr(\"avg(f1) as macro\").show())\n","print (eval_result.selectExpr(\"sum(f1*total) as sumprod\",\"sum(total) as sumtotal\").selectExpr(\"sumprod/sumtotal as micro\").show())"]},{"cell_type":"code","execution_count":105,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7878,"status":"ok","timestamp":1712935630749,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"nh1LY2cwdApE","outputId":"f209c88b-d332-42a9-905e-9ba28e9c96e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-------+------+----+-----+------+---------+------+------+\n","| entity|    tp|  fp|   fn| total|precision|recall|    f1|\n","+-------+------+----+-----+------+---------+------+------+\n","|Disease|1149.0|99.0|348.0|1497.0|   0.9207|0.7675|0.8372|\n","+-------+------+----+-----+------+---------+------+------+\n","\n","+------------------+\n","|             macro|\n","+------------------+\n","|0.8371584699453551|\n","+------------------+\n","\n","None\n","+------------------+\n","|             micro|\n","+------------------+\n","|0.8371584699453551|\n","+------------------+\n","\n","None\n"]}],"source":["evaler = medical.NerDLMetrics(mode=\"partial_chunk_per_token\")\n","\n","eval_result = evaler.computeMetricsFromDF(pred_df.select(\"label\",\"ner\"), prediction_col=\"ner\", label_col=\"label\", drop_o = True, case_sensitive = True).cache()\n","\n","eval_result.withColumn(\"precision\", F.round(eval_result[\"precision\"],4))\\\n","           .withColumn(\"recall\", F.round(eval_result[\"recall\"],4))\\\n","           .withColumn(\"f1\", F.round(eval_result[\"f1\"],4)).show(100)\n","\n","print(eval_result.selectExpr(\"avg(f1) as macro\").show())\n","print (eval_result.selectExpr(\"sum(f1*total) as sumprod\",\"sum(total) as sumtotal\").selectExpr(\"sumprod/sumtotal as micro\").show())"]},{"cell_type":"markdown","metadata":{"id":"_iNzZYVz0P5B"},"source":["`ner_utils`: This new module is used after NER training to calculate mertic chunkbase and plot training logs.\n","\n","`evaluate`: if verbose, returns overall performance, as well as performance per chunk type; otherwise, simply returns overall precision, recall, f1 scores\n","\n","`loss_plot`: Plots the figure of loss vs epochs\n","\n","`get_charts` : Plots the figures of metrics ( precision, recall, f1) vs epochs\n","\n","```\n","import sparknlp_jsl\n","from sparknlp_jsl.training_log_parser import ner_log_parser\n","parser = ner_log_parser()\n","\n","pred_df = ner_model.stages[2].transform(test_data)\n","\n","pred_df = pred_df.select(F.explode(F.arrays_zip(pred_df.ner.result,\n","                                                pred_df.label.result)).alias(\"cols\"))\\\n","                 .select(F.expr(\"cols['0']\").alias(\"prediction\"),\n","                         F.expr(\"cols['1']\").alias(\"ground_truth\"))\n","                 \n","df = pred_df.toPandas()\n","\n","\n","metrics = parser.evaluate( df['prediction'].values, df['ground_truth'].values)\n","\n","parser.loss_plot(f\"./ner_logs/{log_file}\")\n","\n","parser.get_charts('./ner_logs/'+log_file)\n","```"]},{"cell_type":"code","execution_count":106,"metadata":{"id":"SdlTQl4eJmvl","executionInfo":{"status":"ok","timestamp":1712935631581,"user_tz":-180,"elapsed":834,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["ner_model.stages[2].write().overwrite().save('models/custom_NER_model')"]},{"cell_type":"code","execution_count":107,"metadata":{"id":"ZEzG0T1vEZvV","executionInfo":{"status":"ok","timestamp":1712935633952,"user_tz":-180,"elapsed":2374,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["document = nlp.DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","sentence = nlp.SentenceDetector()\\\n","    .setInputCols(['document'])\\\n","    .setOutputCol('sentence')\n","\n","token = nlp.Tokenizer()\\\n","    .setInputCols(['sentence'])\\\n","    .setOutputCol('token')\n","\n","loaded_ner_model = medical.NerModel.load(\"models/custom_NER_model\")\\\n","    .setInputCols([\"sentence\", \"token\", \"embeddings\"])\\\n","    .setOutputCol(\"ner\")\n","\n","converter = medical.NerConverterInternal()\\\n","    .setInputCols([\"document\", \"token\", \"ner\"])\\\n","    .setOutputCol(\"ner_span\")\n","\n","ner_prediction_pipeline = nlp.Pipeline(stages=[\n","    document,\n","    sentence,\n","    token,\n","    clinical_embeddings,\n","    loaded_ner_model,\n","    converter])\n","\n","empty_data = spark.createDataFrame([['']]).toDF(\"text\")\n","\n","prediction_model = ner_prediction_pipeline.fit(empty_data)\n","\n","\n","light_model = nlp.LightPipeline(prediction_model)"]},{"cell_type":"code","execution_count":108,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1712935633953,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"kR4172tKJxX3","outputId":"11a65659-8942-4da1-edc7-57314153bcf9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('metastatic breast cancer', 'Disease')]"]},"metadata":{},"execution_count":108}],"source":["text = \"She has a metastatic breast cancer\"\n","\n","result = light_model.fullAnnotate(text)[0]\n","\n","[(i.result, i.metadata['entity']) for i in result['ner_span']]"]},{"cell_type":"markdown","metadata":{"id":"c_T8KEpnij8c"},"source":["## BertForTokenClassification NER models"]},{"cell_type":"code","execution_count":109,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":47242,"status":"ok","timestamp":1712935681190,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"rkhS3P6Hij8d","outputId":"c2998741-b62d-43d6-f9a3-43a86d420ffd"},"outputs":[{"output_type":"stream","name":"stdout","text":["sentence_detector_dl_healthcare download started this may take some time.\n","Approximate size to download 367.3 KB\n","[OK!]\n","bert_token_classifier_ner_clinical download started this may take some time.\n","[OK!]\n"]}],"source":["documentAssembler = nlp.DocumentAssembler()\\\n","    .setInputCol(\"text\")\\\n","    .setOutputCol(\"document\")\n","\n","sentenceDetector = nlp.SentenceDetectorDLModel.pretrained(\"sentence_detector_dl_healthcare\",\"en\",\"clinical/models\")\\\n","    .setInputCols([\"document\"])\\\n","    .setOutputCol(\"sentence\")\n","\n","tokenizer = nlp.Tokenizer()\\\n","    .setInputCols(\"sentence\")\\\n","    .setOutputCol(\"token\")\n","\n","tokenClassifier = medical.BertForTokenClassification.pretrained(\"bert_token_classifier_ner_clinical\", \"en\", \"clinical/models\")\\\n","    .setInputCols(\"token\", \"sentence\")\\\n","    .setOutputCol(\"ner\")\\\n","    .setCaseSensitive(True)\n","\n","ner_converter = medical.NerConverterInternal()\\\n","    .setInputCols([\"sentence\",\"token\",\"ner\"])\\\n","    .setOutputCol(\"ner_chunk\")\n","\n","pipeline =  nlp.Pipeline(stages=[\n","    documentAssembler,\n","    sentenceDetector,\n","    tokenizer,\n","    tokenClassifier,\n","    ner_converter\n","    ])\n","\n","model = pipeline.fit(spark.createDataFrame([[\"\"]]).toDF(\"text\"))\n","\n","text = \"\"\"A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .\n","Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection .\n","She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation .\n","Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity .\n","Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 .\n","Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia .\n","The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission .\n","However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L .\n","The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again .\n","The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours .\n","Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use .\n","The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day .\n","It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge .\"\"\"\n","\n","res = model.transform(spark.createDataFrame([[text]]).toDF(\"text\"))"]},{"cell_type":"code","execution_count":110,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4616,"status":"ok","timestamp":1712935685797,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"ciii_Ptkij8d","outputId":"c6981c9a-996c-4653-f161-c744ee68e9a8"},"outputs":[{"output_type":"stream","name":"stdout","text":["+-----------+-----------------------------+---+---------+\n","|sentence_id|chunk                        |end|ner_label|\n","+-----------+-----------------------------+---+---------+\n","|0          |gestational diabetes mellitus|67 |PROBLEM  |\n","|0          |type two diabetes mellitus   |153|PROBLEM  |\n","|0          |T2DM                         |160|PROBLEM  |\n","|0          |HTG-induced pancreatitis     |209|PROBLEM  |\n","|0          |an acute hepatitis           |280|PROBLEM  |\n","|0          |obesity                      |294|PROBLEM  |\n","|0          |a body mass index            |317|TEST     |\n","|0          |BMI                          |323|TEST     |\n","|0          |polyuria                     |387|PROBLEM  |\n","|0          |polydipsia                   |400|PROBLEM  |\n","|0          |poor appetite                |416|PROBLEM  |\n","|0          |vomiting                     |431|PROBLEM  |\n","|1          |amoxicillin                  |521|TREATMENT|\n","|1          |a respiratory tract infection|555|PROBLEM  |\n","|2          |metformin                    |578|TREATMENT|\n","|2          |glipizide                    |590|TREATMENT|\n","|2          |dapagliflozin                |610|TREATMENT|\n","|2          |T2DM                         |619|PROBLEM  |\n","|2          |atorvastatin                 |636|TREATMENT|\n","|2          |gemfibrozil                  |652|TREATMENT|\n","+-----------+-----------------------------+---+---------+\n","only showing top 20 rows\n","\n"]}],"source":["res.select(F.explode(F.arrays_zip(res.ner_chunk.result, res.ner_chunk.begin, res.ner_chunk.end, res.ner_chunk.metadata)).alias(\"cols\")) \\\n","   .select(F.expr(\"cols['3']['sentence']\").alias(\"sentence_id\"),\n","           F.expr(\"cols['0']\").alias(\"chunk\"),\n","           F.expr(\"cols['2']\").alias(\"end\"),\n","           F.expr(\"cols['3']['entity']\").alias(\"ner_label\"))\\\n","   .filter(\"ner_label!='O'\")\\\n","   .show(truncate=False)"]},{"cell_type":"code","execution_count":111,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":2632,"status":"ok","timestamp":1712935688419,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"WFw1PgGP_K1S","outputId":"17f9a1f7-8fca-4877-8c2e-4f0fc220e984"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","<style>\n","    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;500;600;700&display=swap');\n","    @import url('https://fonts.googleapis.com/css2?family=Vistol Regular:wght@300;400;500;600;700&display=swap');\n","    \n","    .spark-nlp-display-scroll-entities {\n","        border: 1px solid #E7EDF0;\n","        border-radius: 3px;\n","        text-align: justify;\n","        \n","    }\n","    .spark-nlp-display-scroll-entities span {  \n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #536B76;\n","        font-family: 'Montserrat', sans-serif !important;\n","    }\n","    \n","    .spark-nlp-display-entity-wrapper{\n","    \n","        display: inline-grid;\n","        text-align: center;\n","        border-radius: 4px;\n","        margin: 0 2px 5px 2px;\n","        padding: 1px\n","    }\n","    .spark-nlp-display-entity-name{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        background: #f1f2f3;\n","        border-width: medium;\n","        text-align: center;\n","        \n","        font-weight: 400;\n","        \n","        border-radius: 5px;\n","        padding: 2px 5px;\n","        display: block;\n","        margin: 3px 2px;\n","    \n","    }\n","    .spark-nlp-display-entity-type{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-entity-resolution{\n","        font-size: 14px;\n","        line-height: 24px;\n","        color: #ffffff;\n","        font-family: 'Vistol Regular', sans-serif !important;\n","        \n","        text-transform: uppercase;\n","        \n","        font-weight: 500;\n","\n","        display: block;\n","        padding: 3px 5px;\n","    }\n","    \n","    .spark-nlp-display-others{\n","        font-size: 14px;\n","        line-height: 24px;\n","        font-family: 'Montserrat', sans-serif !important;\n","        \n","        font-weight: 400;\n","    }\n","\n","</style>\n"," <span class=\"spark-nlp-display-others\" style=\"background-color: white\">A 28-year-old female with a history of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">gestational diabetes mellitus </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> diagnosed eight years prior to presentation and subsequent </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">type two diabetes mellitus </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> ( </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">T2DM </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> ), one prior episode of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">HTG-induced pancreatitis </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> three years prior to presentation , associated with </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">an acute hepatitis </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">obesity </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> with </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">a body mass index </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> ( </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">BMI </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> ) of 33.5 kg/m2 , presented with a one-week history of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">polyuria </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">polydipsia </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">poor appetite </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">vomiting </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> .<br>Two weeks prior to presentation , she was treated with a five-day course of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">amoxicillin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">a respiratory tract infection </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> .<br>She was on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">metformin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">glipizide </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">dapagliflozin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">T2DM </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">atorvastatin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">gemfibrozil </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">HTG </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> . She had been on </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">dapagliflozin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for six months at the time of presentation .<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">Physical examination </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> on presentation was significant for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">dry oral mucosa </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> ; significantly , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">her abdominal examination </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was benign with no </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">tenderness </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">guarding </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , or </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">rigidity </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> .<br>Pertinent laboratory findings on admission were : </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">serum glucose </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 111 mg/dl , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">bicarbonate </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 18 mmol/l , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">anion gap </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 20 , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">creatinine </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 0.4 mg/dL , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">triglycerides </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 508 mg/dL , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">total cholesterol </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 122 mg/dL , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">glycated hemoglobin ( </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">HbA1c </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">) </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 10% , and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">venous pH </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 7.27 .<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">Serum lipase </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was normal at 43 U/L . </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">Serum acetone levels </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> could not be assessed as </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">blood samples </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> kept </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">hemolyzing </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> due to </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">significant lipemia </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> .<br>The patient was initially admitted for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">starvation ketosis </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> , as she reported </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">poor oral intake </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for three days prior to admission .<br>However , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">serum chemistry </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> obtained six hours after presentation revealed </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">her glucose </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was 186 mg/dL , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">the anion gap </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was still </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">elevated </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> at 21 , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">serum bicarbonate </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was 16 mmol/L , </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">triglyceride level </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> peaked at 2050 mg/dL , and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">lipase </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was 52 U/L .<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">The β-hydroxybutyrate level </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was obtained and found to be </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">elevated </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> at 5.29 mmol/L - the original sample was centrifuged and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">the chylomicron layer </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> removed prior to </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">analysis </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> due to interference from </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">turbidity </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> caused by </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">lipemia </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> again .<br>The patient was treated with </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">an insulin drip </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> for </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">euDKA </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">HTG </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> with a reduction in </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">the anion gap </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> to 13 and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #77b5fe\"><span class=\"spark-nlp-display-entity-name\">triglycerides </span><span class=\"spark-nlp-display-entity-type\">TEST</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> to 1400 mg/dL , within 24 hours .<br></span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">Her euDKA </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> was thought to be precipitated by </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #800080\"><span class=\"spark-nlp-display-entity-name\">her respiratory tract infection </span><span class=\"spark-nlp-display-entity-type\">PROBLEM</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> in the setting of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">SGLT2 inhibitor use </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> .<br>The patient was seen by the endocrinology service and she was discharged on 40 units of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">insulin glargine </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> at night , 12 units of </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">insulin lispro </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> with meals , and </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">metformin </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> 1000 mg two times a day .<br>It was determined that </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">all </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> </span><span class=\"spark-nlp-display-entity-wrapper\" style=\"background-color: #8b6673\"><span class=\"spark-nlp-display-entity-name\">SGLT2 inhibitors </span><span class=\"spark-nlp-display-entity-type\">TREATMENT</span></span><span class=\"spark-nlp-display-others\" style=\"background-color: white\"> should be discontinued indefinitely . She had close follow-up with endocrinology post discharge .</span></div>"]},"metadata":{}}],"source":["light_model = nlp.LightPipeline(model)\n","\n","light_result = light_model.fullAnnotate(text)\n","\n","\n","visualiser = nlp.viz.NerVisualizer()\n","\n","visualiser.display(light_result[0], label_col='ner_chunk', document_col='document', save_path=\"display_bert_result.html\")"]},{"cell_type":"markdown","metadata":{"id":"M3Whd9kamRqw"},"source":["**Training Clinical BertForTokenClassification Model**\n","\n","For training own BertForTokenClassification NER model, you can check [this notebook](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.5.BertForTokenClassification_NER_SparkNLP_with_Transformers.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"QBDG2m3z_A0M"},"source":["## Pretrained NER Profiling Pipelines\n","\n","We can use pretrained NER profiling pipelines for exploring all the available pretrained NER models at once. In Spark NLP we have two different NER profiling pipelines;\n","\n","- `ner_profiling_clinical` : Returns results for clinical NER models trained with `embeddings_clinical`.\n","- `ner_profiling_biobert` : Returns results for clinical NER models trained with `biobert_pubmed_base_cased`.\n","\n","For more examples, please check [this notebook](https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/11.2.Pretrained_NER_Profiling_Pipelines.ipynb).\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"wUoe5mEEO5Fv"},"source":["<center><b>NER Profiling Clinical Model List</b>\n","\n","|| | | |\n","|--------------|-----------------|-----------------|-----------------|\n","| jsl_ner_wip_clinical               | jsl_ner_wip_greedy_clinical                    | jsl_rd_ner_wip_greedy_clinical   | jsl_ner_wip_modifier_clinical |\n","| ner_abbreviation_clinical          | ner_ade_binary                                 | ner_ade_clinical                 | ner_anatomy          |\n","| ner_anatomy_coarse                  | ner_bacterial_species                          | ner_biomedical_bc2gm             | ner_biomarker        |\n","| ner_bionlp                          | ner_chemicals                                  | ner_chemd_clinical               | ner_chemprot_clinical|\n","| ner_chexpert                        | ner_clinical                                   | ner_clinical_large               | ner_clinical_trials_abstracts  |\n","| ner_covid_trials                    | ner_deid_augmented                             | ner_deid_enriched                | ner_deid_generic_augmented |\n","| ner_deid_large                      | ner_deid_sd                                    | ner_deid_sd_large                | ner_deid_subentity_augmented |\n","| ner_deid_subentity_augmented        | ner_deid_subentity_augmented_i2b2              | ner_diseases                     | ner_diseases_large   |\n","| ner_drugprot_clinical               | ner_drugs                                      | ner_drugs_greedy                 | ner_drugs_large      |\n","| ner_eu_clinical_case                | ner_eu_clinical_condition                       | ner_events_admission_clinical    | ner_events_clinical  |\n","| ner_genetic_variants                | ner_human_phenotype_gene_clinical               | ner_human_phenotype_go_clinical  | ner_jsl              |\n","| ner_jsl_enriched                    | ner_jsl_greedy                                 | ner_jsl_slim                     | ner_living_species   |\n","| ner_measurements_clinical           | ner_medmentions_coarse                          | ner_nature_nero_clinical         | ner_nihss            |\n","| ner_oncology                        | ner_oncology_anatomy_general                    | ner_oncology_anatomy_granular    | ner_oncology_biomarker |\n","| ner_oncology_demographics           | ner_oncology_diagnosis                          | ner_oncology_posology            | ner_oncology_response_to_treatment |\n","| ner_oncology_tnm                    | ner_oncology_therapy                            | ner_oncology_unspecific_posology | ner_oncology_wip     |\n","| ner_pathogen                        | ner_posology                                   | ner_posology_experimental        | ner_posology_greedy  |\n","| ner_posology_large                  | ner_posology_small                              | ner_posology_wip                 | ner_radiology        |\n","| ner_radiology_wip_clinical          | ner_risk_factors                               | ner_sdoh_access_to_healthcare_wip| ner_sdoh_community_condition_wip |\n","| ner_sdoh_demographics_wip           | ner_sdoh_health_behaviours_problems_wip         | ner_sdoh_income_social_status_wip| ner_sdoh_mentions    |\n","| ner_sdoh_social_environment_wip     | ner_sdoh_substance_usage_wip                    | ner_sdoh_wip                     | ner_supplement_clinical |\n","| ner_token                           | ner_vop_anatomy_wip                            | ner_vop_clinical_dept_wip        | ner_vop_demographic_wip |\n","| ner_vop_problem_reduced_wip         | ner_vop_problem_wip                            | ner_vop_slim_wip                 | ner_vop_temporal_wip |\n","| ner_vop_test_wip                    | ner_vop_treatment_wip                           | ner_vop_wip                      |                       |\n","\n","\n","<b>NER Profiling BioBert Model List</b>\n","\n","| | |\n","|-|-|\n","| ner_cellular_biobert           | ner_clinical_biobert             |\n","| ner_diseases_biobert           | ner_anatomy_coarse_biobert       |\n","| ner_events_biobert             | ner_human_phenotype_gene_biobert |\n","| ner_bionlp_biobert             | ner_posology_large_biobert       |\n","| ner_jsl_greedy_biobert         | jsl_rd_ner_wip_greedy_biobert    |\n","| ner_jsl_biobert                | ner_posology_biobert             |\n","| ner_anatomy_biobert            | jsl_ner_wip_greedy_biobert       |\n","| ner_jsl_enriched_biobert       | ner_chemprot_biobert             |\n","| ner_human_phenotype_go_biobert | ner_ade_biobert                  |\n","| ner_deid_biobert               | ner_risk_factors_biobert         |\n","| ner_deid_enriched_biobert      | ner_living_species_biobert                                |\n","\n","\n","</center>"]},{"cell_type":"code","execution_count":112,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":381799,"status":"ok","timestamp":1712936070214,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"y5VEZIdBINmn","outputId":"ee75e666-f7d0-4cb1-e109-29be660abfa0"},"outputs":[{"output_type":"stream","name":"stdout","text":["ner_profiling_clinical download started this may take some time.\n","Approx size to download 2.9 GB\n","[OK!]\n"]}],"source":["clinical_profiling_pipeline = nlp.PretrainedPipeline(\"ner_profiling_clinical\", \"en\", \"clinical/models\")"]},{"cell_type":"code","execution_count":113,"metadata":{"id":"8GYCnYmMnU-H","executionInfo":{"status":"ok","timestamp":1712936070214,"user_tz":-180,"elapsed":11,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["text = '''A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .'''"]},{"cell_type":"code","execution_count":114,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":62801,"status":"ok","timestamp":1712936133004,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"5GJFg2AKpxam","outputId":"09dfd04b-0549-4b99-dc36-a868e42139fd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['ner_vop_problem_wip_chunks', 'ner_sdoh_demographics_wip_chunks', 'ner_ade_clinical_chunks', 'ner_deid_augmented', 'ner_deid_subentity_augmented_i2b2', 'ner_posology_greedy_chunks', 'ner_vop_demographic_wip_chunks', 'ner_sdoh_community_condition_wip_chunks', 'ner_radiology_wip_clinical', 'ner_vop_slim_wip', 'ner_oncology_diagnosis_chunks', 'ner_vop_test_wip_chunks', 'ner_jsl_slim', 'ner_vop_wip', 'ner_risk_factors_chunks', 'jsl_ner_wip_clinical_chunks', 'ner_oncology_unspecific_posology_chunks', 'ner_vop_problem_reduced_wip', 'ner_vop_anatomy_wip', 'ner_vop_problem_reduced_wip_chunks', 'ner_eu_clinical_case_chunks', 'ner_oncology_test_chunks', 'ner_deid_synthetic', 'ner_oncology_posology_chunks', 'ner_oncology_tnm_chunks', 'ner_oncology_anatomy_general_chunks', 'ner_drugs_greedy', 'ner_abbreviation_clinical_chunks', 'ner_covid_trials_chunks', 'ner_human_phenotype_gene_clinical_chunks', 'ner_events_admission_clinical', 'jsl_ner_wip_greedy_clinical_chunks', 'ner_posology_greedy', 'ner_cellular_chunks', 'ner_vop_clinical_dept_wip', 'ner_cancer_genetics_chunks', 'ner_biomedical_bc2gm_chunks', 'ner_oncology', 'ner_jsl_greedy', 'jsl_ner_wip_modifier_clinical_chunks', 'ner_drugs_greedy_chunks', 'ner_sdoh_access_to_healthcare_wip', 'ner_deid_sd_large_chunks', 'ner_diseases_chunks', 'ner_diseases_large', 'ner_chemprot_clinical', 'ner_posology_large', 'nerdl_tumour_demo_chunks', 'ner_oncology_chunks', 'ner_deid_subentity_augmented_chunks', 'ner_jsl_enriched_chunks', 'ner_genetic_variants_chunks', 'ner_sdoh_income_social_status_wip_chunks', 'ner_chexpert', 'ner_bionlp_chunks', 'ner_eu_clinical_condition_chunks', 'ner_measurements_clinical_chunks', 'ner_diseases_large_chunks', 'ner_drugs_large', 'ner_living_species_chunks', 'ner_oncology_therapy_chunks', 'ner_clinical_large', 'ner_chemicals', 'ner_sdoh_slim_wip', 'ner_radiology_chunks', 'ner_bacterial_species', 'ner_deid_augmented_chunks', 'ner_bionlp', 'ner_drugprot_clinical', 'ner_anatomy_chunks', 'ner_deid_subentity_augmented_i2b2_chunks', 'ner_deid_large', 'jsl_ner_wip_modifier_clinical', 'ner_vop_slim_wip_chunks', 'ner_chemprot_clinical_chunks', 'ner_eu_clinical_condition', 'nerdl_tumour_demo', 'ner_posology_experimental_chunks', 'ner_cancer_genetics', 'ner_biomarker', 'ner_drugprot_clinical_chunks', 'ner_vop_problem_wip', 'ner_sdoh_mentions', 'ner_posology_small', 'ner_vop_wip_chunks', 'ner_oncology_biomarker_chunks', 'ner_drugs_chunks', 'ner_deid_sd_chunks', 'ner_chemd_clinical', 'ner_human_phenotype_gene_clinical', 'ner_ade_clinical', 'ner_human_phenotype_go_clinical', 'ner_sdoh_wip_chunks', 'ner_sdoh_social_environment_wip', 'ner_sdoh_mentions_chunks', 'ner_risk_factors', 'ner_clinical', 'ner_posology_large_chunks', 'ner_cellular', 'ner_nature_nero_clinical_chunks', 'ner_deid_large_chunks', 'ner_clinical_trials_abstracts_chunks', 'ner_deid_generic_augmented_chunks', 'ner_posology_chunks', 'ner_oncology_anatomy_general', 'ner_sdoh_social_environment_wip_chunks', 'ner_supplement_clinical_chunks', 'ner_oncology_response_to_treatment_chunks', 'ner_deid_enriched_chunks', 'ner_chemd_clinical_chunks', 'ner_deid_sd_large', 'ner_oncology_biomarker', 'jsl_ner_wip_greedy_clinical', 'ner_sdoh_slim_wip_chunks', 'ner_bacterial_species_chunks', 'ner_sdoh_health_behaviours_problems_wip_chunks', 'ner_sdoh_demographics_wip', 'ner_sdoh_community_condition_wip', 'ner_vop_anatomy_wip_chunks', 'ner_living_species', 'ner_abbreviation_clinical', 'ner_oncology_test', 'ner_vop_temporal_wip_chunks', 'ner_diseases', 'jsl_rd_ner_wip_greedy_clinical', 'ner_oncology_response_to_treatment', 'ner_drugs', 'ner_drugs_large_chunks', 'ner_vop_treatment_wip', 'ner_biomarker_chunks', 'ner_clinical_trials_abstracts', 'ner_clinical_large_chunks', 'ner_anatomy_coarse', 'ner_nature_nero_clinical', 'token', 'ner_oncology_anatomy_granular', 'ner_medmentions_coarse', 'ner_oncology_therapy', 'ner_deid_sd', 'jsl_rd_ner_wip_greedy_clinical_chunks', 'ner_oncology_demographics', 'ner_deid_generic_augmented', 'ner_pathogen_chunks', 'ner_medmentions_coarse_chunks', 'ner_anatomy', 'ner_deid_enriched', 'ner_deid_subentity_augmented', 'ner_radiology_wip_clinical_chunks', 'ner_clinical_chunks', 'ner_oncology_diagnosis', 'ner_sdoh_health_behaviours_problems_wip', 'ner_measurements_clinical', 'ner_ade_binary', 'ner_nihss', 'ner_chexpert_chunks', 'ner_chemicals_chunks', 'ner_pathogen', 'ner_vop_test_wip', 'ner_oncology_demographics_chunks', 'ner_jsl_enriched', 'ner_sdoh_access_to_healthcare_wip_chunks', 'ner_oncology_tnm', 'ner_vop_treatment_wip_chunks', 'ner_deid_synthetic_chunks', 'ner_events_clinical_chunks', 'ner_posology_experimental', 'jsl_ner_wip_clinical', 'ner_oncology_anatomy_granular_chunks', 'ner_biomedical_bc2gm', 'ner_vop_temporal_wip', 'ner_posology_small_chunks', 'ner_oncology_wip', 'ner_nihss_chunks', 'ner_anatomy_coarse_chunks', 'ner_human_phenotype_go_clinical_chunks', 'ner_vop_demographic_wip', 'ner_sdoh_substance_usage_wip_chunks', 'ner_sdoh_income_social_status_wip', 'ner_jsl', 'ner_sdoh_substance_usage_wip', 'ner_events_clinical', 'ner_supplement_clinical', 'ner_jsl_slim_chunks', 'ner_jsl_chunks', 'ner_jsl_greedy_chunks', 'ner_oncology_wip_chunks', 'ner_oncology_unspecific_posology', 'ner_genetic_variants', 'ner_sdoh_wip', 'ner_radiology', 'ner_eu_clinical_case', 'ner_posology', 'ner_vop_clinical_dept_wip_chunks', 'sentence', 'ner_oncology_posology', 'ner_covid_trials', 'ner_ade_binary_chunks', 'ner_events_admission_clinical_chunks'])"]},"metadata":{},"execution_count":114}],"source":["clinical_result = clinical_profiling_pipeline.fullAnnotate(text)[0]\n","clinical_result.keys()"]},{"cell_type":"code","execution_count":115,"metadata":{"id":"C99PTQiNKZtw","executionInfo":{"status":"ok","timestamp":1712936133005,"user_tz":-180,"elapsed":8,"user":{"displayName":"Monster C","userId":"08787989274818793476"}}},"outputs":[],"source":["def get_token_results(light_result):\n","\n","  tokens = [j.result for j in light_result[\"token\"]]\n","  sentences = [j.metadata[\"sentence\"] for j in light_result[\"token\"]]\n","  begins = [j.begin for j in light_result[\"token\"]]\n","  ends = [j.end for j in light_result[\"token\"]]\n","  model_list = [ a for a in light_result.keys() if (a not in [\"sentence\", \"token\"] and \"_chunks\" not in a)]\n","\n","  df = pd.DataFrame({'sentence':sentences, 'begin': begins, 'end': ends, 'token':tokens})\n","\n","  for model_name in model_list:\n","\n","    temp_df = pd.DataFrame(light_result[model_name])\n","    temp_df[\"jsl_label\"] = temp_df.iloc[:,0].apply(lambda x : x.result)\n","    temp_df = temp_df[[\"jsl_label\"]]\n","\n","    # temp_df = get_ner_result(model_name)\n","    temp_df.columns = [model_name]\n","    df = pd.concat([df, temp_df], axis=1)\n","\n","  return df"]},{"cell_type":"code","execution_count":116,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":478},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1712936133005,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"trBEKiZRLiff","outputId":"c14afbc7-43e8-46c9-ccc4-b7f4016fed77"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   sentence  begin  end        token ner_deid_augmented  \\\n","0         0      0    0            A                  O   \n","1         0      2   12  28-year-old                  O   \n","2         0     14   19       female                  O   \n","3         0     21   24         with                  O   \n","4         0     26   26            a                  O   \n","..      ...    ...  ...          ...                ...   \n","68        0    409  416     appetite                  O   \n","69        0    418  418            ,                  O   \n","70        0    420  422          and                  O   \n","71        0    424  431     vomiting                  O   \n","72        0    433  433            .                  O   \n","\n","   ner_deid_subentity_augmented_i2b2 ner_radiology_wip_clinical  \\\n","0                                  O                          O   \n","1                                  O                          O   \n","2                                  O                          O   \n","3                                  O                          O   \n","4                                  O                          O   \n","..                               ...                        ...   \n","68                                 O                  I-Symptom   \n","69                                 O                          O   \n","70                                 O                          O   \n","71                                 O                  B-Symptom   \n","72                                 O                          O   \n","\n","   ner_vop_slim_wip    ner_jsl_slim ner_vop_wip  ... ner_events_clinical  \\\n","0                 O               O           O  ...                   O   \n","1                 O           B-Age       B-Age  ...                   O   \n","2          B-Gender  B-Demographics    B-Gender  ...                   O   \n","3                 O               O           O  ...                   O   \n","4                 O               O           O  ...                   O   \n","..              ...             ...         ...  ...                 ...   \n","68        I-Symptom       I-Symptom   I-Symptom  ...           I-PROBLEM   \n","69                O               O           O  ...                   O   \n","70                O               O           O  ...                   O   \n","71        B-Symptom       B-Symptom   B-Symptom  ...           B-PROBLEM   \n","72                O               O           O  ...                   O   \n","\n","   ner_supplement_clinical ner_oncology_unspecific_posology  \\\n","0                        O                                O   \n","1                        O                                O   \n","2                        O                                O   \n","3                        O                                O   \n","4                        O                                O   \n","..                     ...                              ...   \n","68                       O                                O   \n","69                       O                                O   \n","70                       O                                O   \n","71             B-CONDITION                                O   \n","72                       O                                O   \n","\n","   ner_genetic_variants ner_sdoh_wip ner_radiology  ner_eu_clinical_case  \\\n","0                     O            O             O             B-patient   \n","1               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<td>O</td>\n","      <td>O</td>\n","    </tr>\n","    <tr>\n","      <th>70</th>\n","      <td>0</td>\n","      <td>420</td>\n","      <td>422</td>\n","      <td>and</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>...</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","    </tr>\n","    <tr>\n","      <th>71</th>\n","      <td>0</td>\n","      <td>424</td>\n","      <td>431</td>\n","      <td>vomiting</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>B-Symptom</td>\n","      <td>B-Symptom</td>\n","      <td>B-Symptom</td>\n","      <td>B-Symptom</td>\n","      <td>...</td>\n","      <td>B-PROBLEM</td>\n","      <td>B-CONDITION</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>B-Symptom</td>\n","      <td>B-clinical_condition</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","    </tr>\n","    <tr>\n","      <th>72</th>\n","      <td>0</td>\n","      <td>433</td>\n","      <td>433</td>\n","      <td>.</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>...</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>73 rows × 102 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6cfaad85-c5be-41fa-95ee-14858a89b88a')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            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titlefont=dict(size=30),\n","    yaxis=dict(title_text=\"Clinical NER Models\", titlefont=dict(size=30)),\n",")\n","\n","fig.show()"]},{"cell_type":"markdown","metadata":{"id":"P24PYkhmwTLm"},"source":["## NER Model Finder Pretrained Pipeline\n","`ner_model_finder`  pretrained pipeline trained with bert embeddings that can be used to find the most appropriate NER model given the entity name."]},{"cell_type":"code","execution_count":118,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20922,"status":"ok","timestamp":1712936154533,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"c3L49OPfwOj0","outputId":"d13fbb62-498f-4157-93a6-0e6102090a7c"},"outputs":[{"output_type":"stream","name":"stdout","text":["ner_model_finder download started this may take some time.\n","Approx size to download 148.7 MB\n","[OK!]\n"]}],"source":["finder_pipeline = nlp.PretrainedPipeline(\"ner_model_finder\", \"en\", \"clinical/models\")"]},{"cell_type":"code","execution_count":119,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1712936154533,"user":{"displayName":"Monster C","userId":"08787989274818793476"},"user_tz":-180},"id":"yBEVDDmSAUhC","outputId":"4038ae4a-5a31-426b-ea61-b00dabbe85bc"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['model_names'])"]},"metadata":{},"execution_count":119}],"source":["result = finder_pipeline.fullAnnotate(\"oncology\")[0]\n","result.keys()"]},{"cell_type":"markdown","metadata":{"id":"TVfVAsplCKG4"},"source":["From the metadata in the `model_names` column, we'll get to the top models to the given 'oncology' entity and oncology related categories."]},{"cell_type":"code","execution_count":120,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":25,"status":"ok","timestamp":1712936154533,"user":{"displayName":"Monster 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