2023-04-11-umls_clinical_findings_resolver_pipeline_en (#89)

* Add model 2023-04-11-umls_clinical_findings_resolver_pipeline_en

* Add model 2023-04-11-umls_drug_substance_resolver_pipeline_en

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Co-authored-by: Cabir40 <cabir4006@gmail.com>

docs/_posts/Cabir40/2023-04-11-umls_clinical_findings_resolver_pipeline_en.md

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+---
+layout: model
+title: Clinical Findings to UMLS Code Pipeline
+author: John Snow Labs
+name: umls_clinical_findings_resolver_pipeline
+date: 2023-04-11
+tags: [licensed, clinical, en, umls, pipeline]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.3.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps entities (Clinical Findings) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_clinical_findings_resolver_pipeline_en_4.3.2_3.0_1681216655167.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_clinical_findings_resolver_pipeline_en_4.3.2_3.0_1681216655167.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")
+
+text = 'HTG-induced pancreatitis associated with an acute hepatitis, and obesity'
+
+result = pipeline.annotate(text)
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")
+
+val text = "HTG-induced pancreatitis associated with an acute hepatitis, and obesity"
+
+val result = pipeline.annotate(text)
+```
+</div>
+
+## Results
+
+```bash
++------------------------+---------+---------+
+|chunk                   |ner_label|umls_code|
++------------------------+---------+---------+
+|HTG-induced pancreatitis|PROBLEM  |C1963198 |
+|an acute hepatitis      |PROBLEM  |C4750596 |
+|obesity                 |PROBLEM  |C1963185 |
++------------------------+---------+---------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|umls_clinical_findings_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 4.3.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|4.3 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger

docs/_posts/Cabir40/2023-04-11-umls_drug_substance_resolver_pipeline_en.md

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+---
+layout: model
+title: Drug Substance to UMLS Code Pipeline
+author: John Snow Labs
+name: umls_drug_substance_resolver_pipeline
+date: 2023-04-11
+tags: [licensed, clinical, en, umls, pipeline, drug, subtance]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.3.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps entities (Drug Substances) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_drug_substance_resolver_pipeline_en_4.3.2_3.0_1681217098344.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_drug_substance_resolver_pipeline_en_4.3.2_3.0_1681217098344.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en", "clinical/models")
+
+result = pipeline.annotate("The patient was given  metformin, lenvatinib and Magnesium hydroxide 100mg/1ml")
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en", "clinical/models")
+
+val result = pipeline.annotate("The patient was given  metformin, lenvatinib and Magnesium hydroxide 100mg/1ml")
+```
+
+{:.nlu-block}
+```python
++-----------------------------+---------+---------+
+|chunk                        |ner_label|umls_code|
++-----------------------------+---------+---------+
+|metformin                    |DRUG     |C0025598 |
+|lenvatinib                   |DRUG     |C2986924 |
+|Magnesium hydroxide 100mg/1ml|DRUG     |C1134402 |
++-----------------------------+---------+---------+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|umls_drug_substance_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 4.3.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|5.1 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverter
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger