Mapping UMLS Codes with Their Corresponding ICD10CM Codes

Description

This pretrained model maps UMLS codes to corresponding ICD10CM codes.

Important Note: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains ‘Mapper’, it is necessary to use the ChunkMapperModel after an EntityResolverModel.

Predicted Entities

icd10cm_code

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How to use

document_assembler = DocumentAssembler()\
      .setInputCol("text")\
      .setOutputCol("document")

chunkerMapper = DocMapperModel.pretrained("umls_icd10cm_mapper", "en", "clinical/models")\
      .setInputCols(["document"])\
      .setOutputCol("mappings")\
      .setRels(["icd10cm_code"])

pipeline = Pipeline().setStages([document_assembler,
                                     chunkerMapper])

df = spark.createDataFrame([["C0000744"], ["C2875181"]]).toDF("text")

res = pipeline.fit(df).transform(df)

val document_assembler = new DocumentAssembler()
      .setInputCol("text")
      .setOutputCol("document")

val chunkerMapper = DocMapperModel.pretrained("umls_icd10cm_mapper", "en", "clinical/models")
      .setInputCols("document")
      .setOutputCol("mappings")
      .setRels("icd10cm_code")
    
val pipeline = new Pipeline(stages = Array(
        document_assembler,
        chunkerMapper
))

val data = Seq([["C0000744"], ["C2875181"]]).toDS.toDF("text")

val result= pipeline.fit(data).transform(data)

Results

+---------+------------+------------+
|umls_code|icd10cm_code|relation    |
+---------+------------+------------+
|C0000744 |E786        |icd10cm_code|
|C2875181 |G4381       |icd10cm_code|
+---------+------------+------------+

Model Information