Mapping ICD-10-CM Codes with Corresponding Medicare Severity-Diagnosis Related Group (MS-DRG)

Description

This pretrained model maps ICD-10-CM codes with their corresponding Medicare Severity-Diagnosis Related Group (MS-DRG).

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

ms-drg

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How to use

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

chunkMapper = DocMapperModel.pretrained("icd10cm_ms_drg_mapper", "en", "clinical/models")\
      .setInputCols(["icd_chunk"])\
      .setOutputCol("mappings")\
      .setRels(["ms-drg"])

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

model = pipeline.fit(spark.createDataFrame([['']]).toDF('text')) 

lp = LightPipeline(model)

res = lp.fullAnnotate(["L08.1", "U07.1", "C10.0", "J351"])

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

val chunkMapper = DocMapperModel.pretrained("icd10cm_ms_drg_mapper", "en", "clinical/models")
      .setInputCols(Array("icd_chunk"))
      .setOutputCol("mappings")
      .setRels(Array("ms-drg")) 

val mapper_pipeline = new Pipeline().setStages(Array(document_assembler, chunkMapper))

val data = Seq(Array("L08.1", "U07.1", "C10.0", "J351")).toDS.toDF("text")

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

Results

+----------+-------------------------------+
|icd10_code|ms-drg                         |
+----------+-------------------------------+
|L08.1     |Erythrasma                     |
|U07.1     |COVID-19                       |
|C10.0     |Malignant neoplasm of vallecula|
|J351      |Hypertrophy of tonsils         |
+----------+-------------------------------+

Model Information

References

This model was trained with data from https://www.icd10data.com/ICD10CM/DRG/Amp