Models hub internal

docs/_posts/Ahmetemintek/2023-02-09-rxnorm_drug_brandname_mapper_en.md

@@ -0,0 +1,148 @@
+---
+layout: model
+title: Mapping RxNorm and RxNorm Extension Codes with Corresponding Drug Brand Names
+author: John Snow Labs
+name: rxnorm_drug_brandname_mapper
+date: 2023-02-09
+tags: [chunk_mappig, rxnorm, drug_brand_name, rxnorm_extension, en, clinical, licensed]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.3.0
+spark_version: 3.0
+supported: true
+annotator: ChunkMapperModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained model maps RxNorm and RxNorm Extension codes with their corresponding drug brand names. It returns 2 types of brand names for the corresponding RxNorm or RxNorm Extension code.
+
+## Predicted Entities
+
+`rxnorm_brandname`, `rxnorm_extension_brandname`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/26.Chunk_Mapping.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/rxnorm_drug_brandname_mapper_en_4.3.0_3.0_1675966478332.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/rxnorm_drug_brandname_mapper_en_4.3.0_3.0_1675966478332.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
+documentAssembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("chunk")
+
+sbert_embedder = BertSentenceEmbeddings\
+      .pretrained("sbiobert_base_cased_mli", "en","clinical/models")\
+      .setInputCols(["chunk"])\
+      .setOutputCol("sbert_embeddings")
+
+rxnorm_resolver = SentenceEntityResolverModel\
+      .pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models")\
+      .setInputCols(["chunk", "sbert_embeddings"])\
+      .setOutputCol("rxnorm_code")\
+      .setDistanceFunction("EUCLIDEAN")
+
+resolver2chunk = Resolution2Chunk()\
+    .setInputCols(["rxnorm_code"]) \
+    .setOutputCol("rxnorm_chunk")\
+
+chunkerMapper = ChunkMapperModel.pretrained("rxnorm_drug_brandname_mapper", "en", "clinical/models")\
+      .setInputCols(["rxnorm_chunk"])\
+      .setOutputCol("mappings")\
+      .setRels(["rxnorm_brandname", "rxnorm_extension_brandname"])
+
+
+pipeline = Pipeline(
+    stages = [
+        documentAssembler,
+        sbert_embedder,
+        rxnorm_resolver,
+        resolver2chunk,
+        chunkerMapper
+        ])
+
+model = pipeline.fit(spark.createDataFrame([['']]).toDF('text')) 
+
+pipeline = LightPipeline(model)
+
+result = pipeline.fullAnnotate(['metformin', 'advil'])
+
+```
+```scala
+val documentAssembler = new DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("chunk")
+
+val sbert_embedder = BertSentenceEmbeddings\
+      .pretrained("sbiobert_base_cased_mli", "en","clinical/models")\
+      .setInputCols(["chunk"])\
+      .setOutputCol("sbert_embeddings")
+
+val rxnorm_resolver = SentenceEntityResolverModel\
+      .pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models")\
+      .setInputCols(["chunk", "sbert_embeddings"])\
+      .setOutputCol("rxnorm_code")\
+      .setDistanceFunction("EUCLIDEAN")
+
+val resolver2chunk = new Resolution2Chunk()\
+    .setInputCols(["rxnorm_code"]) \
+    .setOutputCol("rxnorm_chunk")\
+
+val chunkerMapper = ChunkMapperModel.pretrained("rxnorm_drug_brandname_mapper", "en", "clinical/models")\
+      .setInputCols(["rxnorm_chunk"])\
+      .setOutputCol("mappings")\
+      .setRels(["rxnorm_brandname", "rxnorm_extension_brandname"])
+
+
+
+val pipeline = new Pipeline(stages = Array(
+documentAssembler,
+sbert_embedder,
+rxnorm_resolver,
+resolver2chunk
+chunkerMapper
+))
+
+val data = Seq(Array("metformin", "advil")).toDS.toDF("text")
+
+val result= pipeline.fit(data).transform(data)
+
+```
+</div>
+
+## Results
+
+```bash
++--------------+-------------+--------------------------------------------------+--------------------------+
+|     drug_name|rxnorm_result|                                    mapping_result|                 relation |
++--------------+-------------+--------------------------------------------------+--------------------------+
+|     metformin|         6809|Actoplus Met (metformin):::Avandamet (metformin...|          rxnorm_brandname|
+|     metformin|         6809|A FORMIN (metformin):::ABERIN MAX (metformin)::...|rxnorm_extension_brandname|
+|         advil|       153010|                                     Advil (Advil)|          rxnorm_brandname|
+|         advil|       153010|                                              NONE|rxnorm_extension_brandname|
++--------------+-------------+--------------------------------------------------+--------------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|rxnorm_drug_brandname_mapper|
+|Compatibility:|Healthcare NLP 4.3.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[rxnorm_chunk]|
+|Output Labels:|[mappings]|
+|Language:|en|
+|Size:|4.0 MB|

docs/_posts/Cabir40/2023-01-06-redl_clinical_biobert_en.md

@@ -221,4 +221,4 @@ TrIP                0.517     0.796     0.627       151
 TrNAP               0.402     0.672     0.503       112
 TrWP                0.257     0.824     0.392       109
 Avg.                0.635     0.803     0.691      -
-```
+```

docs/_posts/Meryem1425/2023-02-10-ner_sdoh_demographics_wip_en.md

@@ -0,0 +1,166 @@
+---
+layout: model
+title: Extract Demographic Entities from Social Determinants of Health Texts
+author: John Snow Labs
+name: ner_sdoh_demographics_wip
+date: 2023-02-10
+tags: [licensed, clinical, social_determinants, en, ner, demographics, sdoh, public_health]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.8
+spark_version: 3.0
+supported: true
+annotator: MedicalNerModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model extracts demographic information related to Social Determinants of Health from various kinds of biomedical documents.
+
+## Predicted Entities
+
+`Family_Member`, `Age`, `Gender`, `Geographic_Entity`, `Race_Ethnicity`, `Language`, `Spiritual_Beliefs`
+
+{:.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/ner_sdoh_demographics_wip_en_4.2.8_3.0_1675998706136.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ner_sdoh_demographics_wip_en_4.2.8_3.0_1675998706136.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
+document_assembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")\
+    .setInputCols(["document"])\
+    .setOutputCol("sentence")
+
+tokenizer = Tokenizer()\
+    .setInputCols(["sentence"])\
+    .setOutputCol("token")
+
+clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
+    .setInputCols(["sentence", "token"])\
+    .setOutputCol("embeddings")
+
+ner_model = MedicalNerModel.pretrained("ner_sdoh_demographics_wip", "en", "clinical/models")\
+    .setInputCols(["sentence", "token", "embeddings"])\
+    .setOutputCol("ner")
+
+ner_converter = NerConverterInternal()\
+    .setInputCols(["sentence", "token", "ner"])\
+    .setOutputCol("ner_chunk")
+
+pipeline = Pipeline(stages=[
+    document_assembler, 
+    sentence_detector,
+    tokenizer,
+    clinical_embeddings,
+    ner_model,
+    ner_converter   
+    ])
+
+sample_texts = ["SOCIAL HISTORY: He is a former tailor from Korea.",
+             "He lives alone,single and no children.",
+             "Pt is a 61 years old married, Caucasian, Catholic woman. Pt speaks English reasonably well."]
+
+
+data = spark.createDataFrame(sample_texts, StringType()).toDF("text")
+
+result = pipeline.fit(data).transform(data)
+```
+```scala
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")
+    .setInputCols("document")
+    .setOutputCol("sentence")
+
+val tokenizer = new Tokenizer()
+    .setInputCols("sentence")
+    .setOutputCol("token")
+
+val clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
+    .setInputCols(Array("sentence", "token"))
+    .setOutputCol("embeddings")
+
+val ner_model = MedicalNerModel.pretrained("ner_sdoh_demographics_wip", "en", "clinical/models")
+    .setInputCols(Array("sentence", "token", "embeddings"))
+    .setOutputCol("ner")
+
+val ner_converter = new NerConverterInternal()
+    .setInputCols(Array("sentence", "token", "ner"))
+    .setOutputCol("ner_chunk")
+
+val pipeline = new Pipeline().setStages(Array(
+    document_assembler, 
+    sentence_detector,
+    tokenizer,
+    clinical_embeddings,
+    ner_model,
+    ner_converter   
+))
+
+val data = Seq("Pt is a 61 years old married, Caucasian, Catholic woman. Pt speaks English reasonably well.").toDS.toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
++-----------------+-----+---+------------+
+|ner_label        |begin|end|chunk       |
++-----------------+-----+---+------------+
+|Gender           |16   |17 |He          |
+|Geographic_Entity|43   |47 |Korea       |
+|Gender           |0    |1  |He          |
+|Family_Member    |29   |36 |children    |
+|Age              |8    |19 |61 years old|
+|Race_Ethnicity   |30   |38 |Caucasian   |
+|Spiritual_Beliefs|41   |48 |Catholic    |
+|Gender           |50   |54 |woman       |
+|Language         |67   |73 |English     |
++-----------------+-----+---+------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ner_sdoh_demographics_wip|
+|Compatibility:|Healthcare NLP 4.2.8+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence, token, embeddings]|
+|Output Labels:|[ner]|
+|Language:|en|
+|Size:|858.4 KB|
+
+## Benchmarking
+
+```bash
+	    label	     tp	    fp	   fn	   total	precision	   recall	       f1
+              Age	 1346.0	  73.0	 74.0	  1420.0	 0.948555	 0.947887	 0.948221
+Spiritual_Beliefs	  100.0	  13.0	 16.0	   116.0	 0.884956	 0.862069	 0.873362
+    Family_Member	 4468.0	 134.0	 43.0	  4511.0	 0.970882	 0.990468	 0.980577
+   Race_Ethnicity	   56.0	   0.0	 13.0	    69.0	 1.000000	 0.811594	 0.896000
+           Gender	 9825.0	  67.0	247.0	 10072.0	 0.993227	 0.975477	 0.984272
+Geographic_Entity	  225.0	   9.0	 29.0	   254.0	 0.961538	 0.885827	 0.922131
+         Language	   51.0	   9.0	  5.0	    56.0	 0.850000	 0.910714	 0.879310
+```