doc fix in old hc md files (#13025)

* doc fix in old hc md files * Update 2021-03-29-recognize_entities_posology_en.md * Update 2021-01-18-ner_radiology_en.md * Update 2022-10-01-assertion_oncology_demographic_binary_wip_en.md * Update 2021-01-29-deidentify_enriched_clinical_en.md * updates * Update 2021-01-29-ner_drugs_large_en.md and 2021-07-23-cantemist_scielowiki_es.md * Update 2021-01-29-deidentify_enriched_clinical_en.md * Update 2021-01-18-ner_radiology_en.md * Update 2021-01-18-re_bodypart_directions_en.md * Update 2021-01-18-re_bodypart_directions_en.md * Update 2021-01-18-re_bodypart_problem_en.md * Update 2021-01-18-re_bodypart_proceduretest_en.md * Update 2021-01-18-re_date_clinical_en.md * Update 2021-01-20-ner_deid_augmented_en.md * Update 2021-01-18-re_bodypart_directions_en.md * Update 2021-01-18-re_bodypart_problem_en.md * Update 2021-01-18-re_bodypart_directions_en.md * Update 2021-01-18-re_bodypart_problem_en.md * Update 2022-07-28-bert_token_classifier_ner_pathogen_en_3_0.md * Update 2022-08-13-ner_negation_uncertainty_es_3_0.md * Update 2022-08-13-ner_pharmacology_es_3_0.md * Update 2022-08-14-disease_mentions_tweet_es_3_0.md * Update 2022-09-29-redl_oncology_location_biobert_wip_en.md * Update 2022-09-30-ner_oncology_diagnosis_wip_en.md * Update 2022-10-01-assertion_oncology_demographic_binary_wip_en.md * Update 2022-10-01-assertion_oncology_family_history_wip_en.md * Update 2022-10-01-assertion_oncology_problem_wip_en.md * Update 2022-10-01-assertion_oncology_response_to_treatment_wip_en.md * Update 2022-10-01-assertion_oncology_smoking_status_wip_en.md * Update 2022-10-01-assertion_oncology_test_binary_wip_en.md * Update 2022-10-01-assertion_oncology_treatment_binary_wip_en.md * Update 2022-10-01-assertion_oncology_wip_en.md * Update 2022-10-11-assertion_oncology_demographic_binary_wip_en.md * Update 2022-10-11-assertion_oncology_family_history_wip_en.md * Update 2022-10-11-assertion_oncology_problem_wip_en.md * Update 2022-10-11-assertion_oncology_response_to_treatment_wip_en.md * Update 2022-10-11-assertion_oncology_smoking_status_wip_en.md * Update 2022-10-11-assertion_oncology_treatment_binary_wip_en.md * Update 2022-10-11-assertion_oncology_wip_en.md * Update 2022-09-29-redl_oncology_location_biobert_wip_en.md * Update 2022-10-01-assertion_oncology_family_history_wip_en.md * Update 2022-10-01-assertion_oncology_problem_wip_en.md * Update 2022-03-22-bert_token_classifier_drug_development_trials_en_3_0.md * 2022-06-26-drug_brandname_ndc_mapper_en_3_0.md * Update 2022-06-26-icd10cm_snomed_mapper_en_3_0.md * update 2022-06-26-icd10cm_umls_mapper_en_3_0.md * Update 2022-06-26-icdo_snomed_mapper_en_3_0.md * Update 2022-06-22-ner_living_species_bert_pt_3_0.md * Update 2022-06-22-ner_living_species_bert_es_3_0.md * Update 2022-06-22-ner_living_species_bert_pt_3_0.md * Update 2022-06-22-ner_living_species_biobert_en_3_0.md * Update 2022-06-22-ner_living_species_en_3_0.md * Update 2022-06-22-ner_living_species_es_3_0.md * Update 2022-06-22-ner_living_species_pt_3_0.md * Update 2022-06-22-ner_living_species_roberta_es_3_0.md * Update 2022-06-22-ner_living_species_roberta_pt_3_0.md * Updated md * Updates * Update 2021-01-18-re_bodypart_directions_en.md Co-authored-by: Cabir C <64752006+Cabir40@users.noreply.github.com> Co-authored-by: Vildan <64216738+Meryem1425@users.noreply.github.com>
JohnSnowLabs · Nov 10, 2022 · ef593c8 · ef593c8
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diff --git a/docs/_posts/C-K-Loan/2021-03-29-recognize_entities_posology_en.md b/docs/_posts/C-K-Loan/2021-03-29-recognize_entities_posology_en.md
@@ -1,97 +1,98 @@
----
-layout: model
-title: Recognize Posology Pipeline
-author: John Snow Labs
-name: recognize_entities_posology
-date: 2021-03-29
-tags: [ner, named_entity_recognition, pos, parts_of_speech, posology, ner_posology, pipeline, en, licensed]
-task: [Named Entity Recognition, Part of Speech Tagging]
-language: en
-edition: Healthcare NLP 3.0.0
-spark_version: 3.0
-supported: true
-article_header:
-type: cover
-use_language_switcher: "Python-Scala-Java"
----
-
-## Description
-
-This pipeline detects drugs, dosage, form, frequency, duration, route, and drug strength in text.
-
-## Predicted Entities
-`DRUG`, `STRENGTH`, `DURATION`, `FREQUENCY`, `FORM`, `DOSAGE`, `ROUTE`.
-
-{:.btn-box}
-[Live Demo](https://demo.johnsnowlabs.com/healthcare/NER_POSOLOGY/){:.button.button-orange}
-[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.Clinical_Named_Entity_Recognition_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/recognize_entities_posology_en_3.0.0_3.0_1617042229126.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
-
-## How to use
-
-
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipelinein
-pipeline = PretrainedPipeline('recognize_entities_posology', lang = 'en')
-annotations =  pipeline.fullAnnotate(""The patient was perscriped 50MG penicilin for is headache"")[0]
-annotations.keys()
-
-```
-```scala
-
-val pipeline = new PretrainedPipeline("recognize_entities_posology", lang = "en")
-val result = pipeline.fullAnnotate("The patient was perscriped 50MG penicilin for is headache")(0)
-
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-result_df = nlu.load('ner.posology').predict("The patient was perscriped 50MG penicilin for is headache")
-result_df
-
-```
-</div>
-
-## Results
-
-```bash
-+-----------------------------------------+
-|result                                   |
-+-----------------------------------------+
-|[O, O, O, O, B-Strength, B-Drug, O, O, O]|
-+-----------------------------------------+
-
-+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-|ner                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
-+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-|[[named_entity, 0, 2, O, [word -> The, confidence -> 1.0], []], [named_entity, 4, 10, O, [word -> patient, confidence -> 0.9993], []], [named_entity, 12, 14, O, [word -> was, confidence -> 1.0], []], [named_entity, 16, 25, O, [word -> perscriped, confidence -> 0.9985], []], [named_entity, 27, 30, B-Strength, [word -> 50MG, confidence -> 0.9966], []], [named_entity, 32, 40, B-Drug, [word -> penicilin, confidence -> 0.9934], []], [named_entity, 42, 44, O, [word -> for, confidence -> 0.9999], []], [named_entity, 46, 47, O, [word -> is, confidence -> 0.9468], []], [named_entity, 49, 56, O, [word -> headache, confidence -> 0.9805], []]]|
-+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-```
-
-{:.model-param}
-## Model Information
-
-{:.table-model}
-|---|---|
-|Model Name:|recognize_entities_posology|
-|Type:|pipeline|
-|Compatibility:|Healthcare NLP 3.0.0+|
-|License:|Licensed|
-|Edition:|Official|
-|Language:|en|
-
-## Included Models
-
-- DocumentAssembler
-- SentenceDetector
-- TokenizerModel
-- WordEmbeddingsModel
-- NerDLModel
-- NerConverter
+---
+layout: model
+title: Recognize Posology Pipeline
+author: John Snow Labs
+name: recognize_entities_posology
+date: 2021-03-29
+tags: [ner, named_entity_recognition, pos, parts_of_speech, posology, ner_posology, pipeline, en, licensed]
+task: [Named Entity Recognition, Part of Speech Tagging]
+language: en
+edition: Healthcare NLP 3.0.0
+spark_version: 3.0
+supported: true
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pipeline detects drugs, dosage, form, frequency, duration, route, and drug strength in text.
+
+## Predicted Entities
+`DRUG`, `STRENGTH`, `DURATION`, `FREQUENCY`, `FORM`, `DOSAGE`, `ROUTE`.
+
+{:.btn-box}
+[Live Demo](https://demo.johnsnowlabs.com/healthcare/NER_POSOLOGY/){:.button.button-orange}
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.Clinical_Named_Entity_Recognition_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/recognize_entities_posology_en_3.0.0_3.0_1617042229126.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipelinein
+pipeline = PretrainedPipeline('recognize_entities_posology', lang = 'en')
+annotations =  pipeline.fullAnnotate(""The patient was perscriped 50MG penicilin for is headache"")[0]
+annotations.keys()
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("recognize_entities_posology", lang = "en")
+val result = pipeline.fullAnnotate("The patient was perscriped 50MG penicilin for is headache")(0)
+
+```
+
+{:.nlu-block}
+```python
+import nlu
+
+result_df = nlu.load('ner.posology').predict("The patient was perscriped 50MG penicilin for is headache")
+result_df
+
+```
+</div>
+
+## Results
+
+```bash
++-----------------------------------------+
+|result                                   |
++-----------------------------------------+
+|[O, O, O, O, B-Strength, B-Drug, O, O, O]|
++-----------------------------------------+
+
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|ner                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|[[named_entity, 0, 2, O, [word -> The, confidence -> 1.0], []], [named_entity, 4, 10, O, [word -> patient, confidence -> 0.9993], []], [named_entity, 12, 14, O, [word -> was, confidence -> 1.0], []], [named_entity, 16, 25, O, [word -> perscriped, confidence -> 0.9985], []], [named_entity, 27, 30, B-Strength, [word -> 50MG, confidence -> 0.9966], []], [named_entity, 32, 40, B-Drug, [word -> penicilin, confidence -> 0.9934], []], [named_entity, 42, 44, O, [word -> for, confidence -> 0.9999], []], [named_entity, 46, 47, O, [word -> is, confidence -> 0.9468], []], [named_entity, 49, 56, O, [word -> headache, confidence -> 0.9805], []]]|
++---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|recognize_entities_posology|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 3.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- NerDLModel
+- NerConverter
+
diff --git a/...Damla-Gurbaz/2022-03-22-bert_token_classifier_drug_development_trials_en_3_0.md b/...Damla-Gurbaz/2022-03-22-bert_token_classifier_drug_development_trials_en_3_0.md
@@ -87,12 +87,12 @@ val documentAssembler = DocumentAssembler()
 
 
 val sentenceDetector = SentenceDetectorDLModel.pretrained()
-.setInputCols("document") 
+.setInputCols(Array("document"))
 .setOutputCol("sentence") 
 
 
 val tokenizer = new Tokenizer()
-.setInputCols("sentence")
+.setInputCols(Array("sentence"))
 .setOutputCol("token")
 
 

diff --git a/docs/_posts/Damla-Gurbaz/2022-06-26-drug_brandname_ndc_mapper_en_3_0.md b/docs/_posts/Damla-Gurbaz/2022-06-26-drug_brandname_ndc_mapper_en_3_0.md
@@ -64,7 +64,7 @@ val document_assembler = new DocumentAssembler()
 .setOutputCol("chunk")
 
 val chunkerMapper = ChunkMapperModel.pretrained("drug_brandname_ndc_mapper", "en", "clinical/models")
-.setInputCols("chunk")
+.setInputCols(Array("chunk"))
 .setOutputCol("ndc")
 .setRels(Array("Strength_NDC"))
 .setLowerCase(True)

diff --git a/docs/_posts/Damla-Gurbaz/2022-06-26-icd10cm_snomed_mapper_en_3_0.md b/docs/_posts/Damla-Gurbaz/2022-06-26-icd10cm_snomed_mapper_en_3_0.md
@@ -73,7 +73,7 @@ val documentAssembler = new DocumentAssembler()
 .setOutputCol("ner_chunk")
 
 val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
-.setInputCols("ner_chunk")
+.setInputCols(Array("ner_chunk"))
 .setOutputCol("sbert_embeddings")
 
 val icd_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented_billable_hcc", "en", "clinical/models")
@@ -82,7 +82,7 @@ val icd_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10
 .setDistanceFunction("EUCLIDEAN")
 
 val chunkerMapper = ChunkMapperModel.pretrained("icd10cm_snomed_mapper", "en","clinical/models")
-.setInputCols("icd10cm_code")
+.setInputCols(Array("icd10cm_code"))
 .setOutputCol("mappings")
 .setRels(Array("snomed_code"))
 

diff --git a/docs/_posts/Damla-Gurbaz/2022-06-26-icd10cm_umls_mapper_en_3_0.md b/docs/_posts/Damla-Gurbaz/2022-06-26-icd10cm_umls_mapper_en_3_0.md
@@ -77,7 +77,7 @@ val documentAssembler = new DocumentAssembler()
 
 val sbert_embedder = BertSentenceEmbeddings
 .pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
-.setInputCols("ner_chunk")
+.setInputCols(Array("ner_chunk"))
 .setOutputCol("sbert_embeddings")
 
 val icd10cm_resolver = SentenceEntityResolverModel
@@ -88,7 +88,7 @@ val icd10cm_resolver = SentenceEntityResolverModel
 
 val chunkerMapper = ChunkMapperModel
 .pretrained("icd10cm_umls_mapper", "en", "clinical/models")
-.setInputCols("rxnorm_code")
+.setInputCols(Array("rxnorm_code"))
 .setOutputCol("umls_mappings")
 .setRels(Array("umls_code"))
 

diff --git a/docs/_posts/Damla-Gurbaz/2022-06-26-icdo_snomed_mapper_en_3_0.md b/docs/_posts/Damla-Gurbaz/2022-06-26-icdo_snomed_mapper_en_3_0.md
@@ -78,7 +78,7 @@ val documentAssembler = new DocumentAssembler()
 
 val sbert_embedder = BertSentenceEmbeddings
 .pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
-.setInputCols("ner_chunk")
+.setInputCols(Array("ner_chunk"))
 .setOutputCol("sbert_embeddings")
 
 val icdo_resolver = SentenceEntityResolverModel
@@ -89,7 +89,7 @@ val icdo_resolver = SentenceEntityResolverModel
 
 val chunkerMapper = ChunkMapperModel
 .pretrained("icdo_snomed_mapper", "en", "clinical/models")
-.setInputCols("icdo_code")
+.setInputCols(Array("icdo_code"))
 .setOutputCol("snomed_mappings")
 .setRels(Array("snomed_code"))
 

diff --git a/docs/_posts/Damla-Gurbaz/2022-06-28-drug_action_treatment_mapper_en_3_0.md b/docs/_posts/Damla-Gurbaz/2022-06-28-drug_action_treatment_mapper_en_3_0.md
@@ -68,7 +68,7 @@ chunkerMapper_action = ChunkMapperModel.pretrained("drug_action_treatment_mapper
 .setRels(["action"])\
 .setLowerCase(True)
 
-chunkerMapper_treatment = ChunkMapperModel.pretrained("drug_action_treatment_mapper", , "en", "clinical/models")\
+chunkerMapper_treatment = ChunkMapperModel.pretrained("drug_action_treatment_mapper", "en", "clinical/models")\
 .setInputCols(["ner_chunk"])\
 .setOutputCol("treatment_mappings")\
 .setRels(["treatment"])\
@@ -126,7 +126,7 @@ val chunkerMapper_action = ChunkMapperModel.pretrained("drug_action_treatment_ma
 .setRels(Array("action"))
 .setLowerCase(True)
 
-val chunkerMapper_treatment = ChunkMapperModel.pretrained("drug_action_treatment_mapper", , "en", "clinical/models")
+val chunkerMapper_treatment = ChunkMapperModel.pretrained("drug_action_treatment_mapper", "en", "clinical/models")
 .setInputCols(Array("ner_chunk"))
 .setOutputCol("treatment_mappings")
 .setRels(Array("treatment"))