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  • John Snow Labs
  • Sep 30, 2022

Mapping ICD-9-CM Codes with Their Corresponding ICD-10-CM Codes

  • en
  • clinical
  • chunk_mapping
  • icd9
  • icd10
  • licensed

Description

This pretrained model maps ICD-9-CM codes to corresponding ICD-10-CM 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

icd10_code

Open in Colab Copy S3 URI

How to use

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

chunk_assembler = Doc2Chunk()\
      .setInputCols(["doc"])\
      .setOutputCol("ner_chunk")
 
chunkerMapper = ChunkMapperModel\
    .pretrained("icd9_icd10_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["icd10_code"])


mapper_pipeline = Pipeline(stages=[
    document_assembler,
    chunk_assembler,
    chunkerMapper
])


model = mapper_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))

light_pipeline= LightPipeline(model)

result = light_pipeline.fullAnnotate("00322")

val documentAssembler = new DocumentAssembler()
        .setInputCol("text")
        .setOutputCol("ner_chunk")

val chunk_assembler = new Doc2Chunk()
        .setInputCols(Array("doc"))
        .setOutputCol("ner_chunk")

val chunkerMapper = ChunkMapperModel.pretrained("icd9_icd10_mapper", "en","clinical/models")
        .setInputCols(Array("ner_chunk"))
        .setOutputCol("mappings")
        .setRels(Array("icd10_code"))
        
val pipeline = new Pipeline(stages = Array(
        documentAssembler,
        chunk_assembler,
        chunkerMapper))

val data = Seq("00322").toDS.toDF("text")

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

import nlu
nlu.load("en.map_entity.icd9_icd10").predict("""Put your text here.""")

Results

+---------+-------------+
|icd9_code|icd10_mapping|
+---------+-------------+
|00322    |A0222        |
+---------+-------------+

Model Information

Model Name: icd9_icd10_mapper
Compatibility: Healthcare NLP 4.1.0+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 323.6 KB