2023-02-09-rxnorm_drug_brandname_mapper_en

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|