Mapping UMLS Codes with Their Corresponding RxNorm Codes

Description

This pretrained model maps UMLS codes to corresponding RxNorm codes.

Predicted Entities

Open in Colab Copy S3 URI

How to use

documentAssembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("ner_chunk")

sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("sbert_embeddings")\
    .setCaseSensitive(False)

umls_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_umls_drug_substance", "en", "clinical/models")\
    .setInputCols(["sbert_embeddings"]) \
    .setOutputCol("umls_code")\
    .setDistanceFunction("EUCLIDEAN")

resolver2chunk = Resolution2Chunk()\
    .setInputCols(["umls_code"])\
    .setOutputCol("umls2chunk")

chunkerMapper = ChunkMapperModel.pretrained("umls_rxnorm_mapper", "en", "clinical/models")\
    .setInputCols(["umls2chunk"])\
    .setOutputCol("mappings")

pipeline = Pipeline(stages = [
    documentAssembler,
    sbert_embedder,
    umls_resolver,
    resolver2chunk,
    chunkerMapper])

data = spark.createDataFrame([['Hydrogen peroxide 30 mg'], ['magnesium hydroxide 100 MG'], ['metformin 1000 MG'], ['dilaudid']]).toDF("text")

mapper_model = pipeline.fit(data)
result = mapper_model.transform(data)
val documentAssembler = DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("ner_chunk")

val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
    .setInputCols(Array("ner_chunk"))
    .setOutputCol("sbert_embeddings")
    .setCaseSensitive(False)

val umls_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_umls_drug_substance", "en", "clinical/models")
    .setInputCols(Array("sbert_embeddings"))
    .setOutputCol("umls_code")
    .setDistanceFunction("EUCLIDEAN")

val resolver2chunk = Resolution2Chunk()
    .setInputCols(Array("umls_code"))
    .setOutputCol("umls2chunk")

val chunkerMapper = ChunkMapperModel.pretrained("umls_rxnorm_mapper", "en", "clinical/models")\
    .setInputCols(Array("umls2chunk"))
    .setOutputCol("mappings")
    	
val mapper_pipeline = new Pipeline().setStages(Array( 
    document_assembler,
    sbert_embedder,
    umls_resolver,
    resolver2chunk,
    chunkerMapper))

val data = Seq(
  ("amlodipine 5 MG"),
  ("magnesium hydroxide 100 MG"),
  ("metformin 1000 MG"),
  ("dilaudid")
).toDF("text")

val mapper_model = mapper_pipeline.fit(data)
result= mapper_model.transform(data)

Results

+--------------------------+---------+-----------+
|chunk                     |umls_code|rxnorm_code|
+--------------------------+---------+-----------+
|Hydrogen peroxide 30 mg   |C1126248 |330565     |
|magnesium hydroxide 100 MG|C1134402 |337012     |
|metformin 1000 MG         |C0987664 |316255     |
|dilaudid                  |C0728755 |224913     |
+--------------------------+---------+-----------+

Model Information

Model Name: umls_rxnorm_mapper
Compatibility: Healthcare NLP 5.3.0+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 3.0 MB