Mapping UMLS Codes with Their Corresponding SNOMED Codes

Description

This pretrained model maps UMLS codes to corresponding SNOMED codes.

Predicted Entities

snomed

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_clinical_drugs", "en", "clinical/models")\
    .setInputCols(["sbert_embeddings"]) \
    .setOutputCol("umls_code")\
    .setDistanceFunction("EUCLIDEAN")

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

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

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

data = spark.createDataFrame([["acebutolol"],["aspirin"]]).toDF("text")

mapper_model = pipeline.fit(data)
result= mapper_model.transform(data)  
val documentAssembler = new 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_clinical_drugs","en","clinical/models")
    .setInputCols(Array("sbert_embeddings"))
    .setOutputCol("umls_code")
    .setDistanceFunction("EUCLIDEAN")
	
val resolver2chunk = new Resolution2Chunk()
    .setInputCols(Array("umls_code"))
    .setOutputCol("umls2chunk")
	
val chunkerMapper = ChunkMapperModel.pretrained("umls_snomed_mapper","en","clinical/models")
    .setInputCols(Array("umls2chunk"))
    .setOutputCol("mappings")
    .setRels(["snomed_code"])
	
val Pipeline(stages = Array(
  documentAssembler,
  sbert_embedder, 
  umls_resolver,
  resolver2chunk,
  chunkerMapper))

val data = Seq("acebutolol", "aspirin").toDF("text")
	
val mapper_model = pipeline.fit(data)

result= mapper_model.transform(data)

Results

+----------+---------+-----------+
|chunk     |umls_code|snomed_code|
+----------+---------+-----------+
|acebutolol|C0000946 |68088000   |
|aspirin   |C0004057 |319770009  |
+----------+---------+-----------+

Model Information

Model Name: umls_snomed_mapper
Compatibility: Healthcare NLP 5.2.1+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 6.9 MB