2021-11-04-sbiobertresolve_rxnorm_augmented_en

docs/_posts/galiph/2021-11-04-sbiobertresolve_rxnorm_augmented_en.md

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+---
+layout: model
+title: Sentence Entity Resolver for RxNorm (sbiobert_base_cased_mli embeddings)
+author: John Snow Labs
+name: sbiobertresolve_rxnorm_augmented
+date: 2021-11-04
+tags: [rxnorm, licensed, en, clinical, entity_resolution]
+task: Entity Resolution
+language: en
+edition: Spark NLP for Healthcare 3.3.1
+spark_version: 2.4
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model maps clinical entities and concepts (like drugs/ingredients) to RxNorm codes using `sbiobert_base_cased_mli` Sentence Bert Embeddings. It trained on the augmented version of the dataset which is used in previous RxNorm resolver models. Additionally, this model returns concept classes of the drugs in `all_k_aux_labels` column.
+
+## Predicted Entities
+
+`RxNorm Codes`, `Concept Classes`
+
+{:.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/3.Clinical_Entity_Resolvers.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/sbiobertresolve_rxnorm_augmented_en_3.3.1_2.4_1636032568821.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("ner_chunk")
+
+sbert_embedder = BertSentenceEmbeddings.pretrained('sbiobert_base_cased_mli', 'en','clinical/models')\
+      .setInputCols(["ner_chunk"])\
+      .setOutputCol("sbert_embeddings")
+
+rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models") \
+      .setInputCols(["ner_chunk", "sbert_embeddings"]) \
+      .setOutputCol("rxnorm_code")\
+      .setDistanceFunction("EUCLIDEAN")
+
+rxnorm_pipelineModel = PipelineModel(
+    stages = [
+        documentAssembler,
+        sbert_embedder,
+        rxnorm_resolver])
+
+clinical_note = """ She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, aspirin 81 mg daily, Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily."""
+```
+```scala
+val documentAssembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("ner_chunk")
+
+val sbert_embedder = BertSentenceEmbeddings.pretrained('sbiobert_base_cased_mli', 'en','clinical/models')\
+      .setInputCols("ner_chunk")\
+      .setOutputCol("sbert_embeddings")
+
+val rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models") \
+      .setInputCols(Array("ner_chunk", "sbert_embeddings")) \
+      .setOutputCol("rxnorm_code")\
+      .setDistanceFunction("EUCLIDEAN")
+
+val rxnorm_pipelineModel = new PipelineModel().setStages(Array(documentAssembler, sbert_embedder, rxnorm_resolver))
+
+val data = Seq("She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, 
+levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, aspirin 81 mg daily, Neurontin 400 mg p.o. t.i.d., 
+Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily.").toDF("text")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
++-----------------+-----+---+---------------+----------+----------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+
+|            chunk|begin|end|         entity|confidence|RxNormCode|                                         all_codes|                                       resolutions|                                     Concept Class|
++-----------------+-----+---+---------------+----------+----------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+
+|       folic acid|  121|130|Drug_Ingredient|   0.59705|      4511|4511:::1162058:::1162059:::62356:::1376005:::54...|folic acid:::folic acid oral product:::folic ac...|Ingredient:::Clinical Dose Group:::Clinical Dos...|
+|    levothyroxine|  144|156|Drug_Ingredient|    0.6059|     10582|10582:::1868004:::40144:::1602753:::1602745:::2...|levothyroxine:::levothyroxine injection:::levot...|Ingredient:::Clinical Drug Form:::Precise Ingre...|
+|          aspirin|  219|225|Drug_Ingredient|    0.9814|      1191|1191:::405403:::218266:::1154070:::215568:::202...|aspirin:::ysp aspirin:::med aspirin:::aspirin p...|Ingredient:::Brand Name:::Brand Name:::Clinical...|
+|magnesium citrate|  313|329|Drug_Ingredient|   0.53295|     52356|52356:::29155:::1314220:::1006900:::52358:::291...|magnesium citrate:::magnesium carbonate:::magne...|Ingredient:::Ingredient:::Precise Ingredient:::...|
+|          insulin|  376|382|Drug_Ingredient|    0.4832|    139825|139825:::1740938:::274783:::86009:::1605101:::5...|insulin detemir:::insulin argine:::insulin glar...|Ingredient:::Ingredient:::Ingredient:::Ingredie...|
++-----------------+-----+---+---------------+----------+----------+--------------------------------------------------+--------------------------------------------------+--------------------------------------------------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|sbiobertresolve_rxnorm_augmented|
+|Compatibility:|Spark NLP for Healthcare 3.3.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[rxnorm_code]|
+|Language:|en|
+|Case sensitive:|false|