Mapping ICD-10-CM Codes with Corresponding Billable and Hierarchical Condition Category (HCC) Scores

Description

This pretrained model maps ICD-10-CM codes with their corresponding billable and HCC scores. If there is no HCC score for the corresponding ICD-10-CM code, result will be returned as 0.

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

billable, hcc_score

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How to use

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

docMapper= DocMapperModel().pretrained("icd10cm_billable_hcc_mapper", "en", "clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("mappings")\
    .setRels(["billable", "hcc_score"]) \
    .setLowerCase(True) \
    .setMultivaluesRelations(True)

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


data = spark.createDataFrame([["D66"], ["S22.00"], ["Z3A.10"]]).toDF("text")

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

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

val docMapper = DocMapperModel.pretrained("icd10cm_billable_hcc_mapper", "en", "clinical/models")
    .setInputCols("document")
    .setOutputCol("mappings")
    .setRels(Array(["billable", "hcc_score"]))

val pipeline = new Pipeline().setStages(Array(
    document_assembler,
    docMapper))


val data = Seq(["D66"], ["S22.00"], ["Z3A.10"]).toDS.toDF("text")

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

Results

+--------------+----------+-----------+
| icd10cm_code | billable | hcc_score |
+--------------+----------+-----------+
| D66          | 1        | 46        |
| S22.00       | 0        | 0         |
| Z3A.10       | 1        | 0         |
+--------------+----------+-----------+

Model Information