Mapping ICD10-CM Codes with Their Corresponding SNOMED Codes

Description

This model maps ICD10-CM codes to their corresponding SNOMED codes.

It performs a direct lookup against the training dictionary, providing fast, exact-match code mapping.

Trained on SNOMED CT US Edition 20260901 crosswalk data.

Live Demo Open in Colab Copy S3 URI

How to use

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

chunkAssembler = Doc2Chunk()\
    .setInputCols(["document"])\
    .setOutputCol("icd10cm_code")

mapper = ChunkMapperModel.pretrained("icd10cm_snomed_mapper_20260901", "en", "clinical/models")\
    .setInputCols(["icd10cm_code"])\
    .setOutputCol("mappings")\
    .setRels(["snomed_code"])

pipeline = Pipeline(stages=[documentAssembler, chunkAssembler, mapper])

data = spark.createDataFrame([["A02.20"]]).toDF("text")
result = pipeline.fit(data).transform(data)
documentAssembler = nlp.DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

chunkAssembler = nlp.Doc2Chunk()\
    .setInputCols(["document"])\
    .setOutputCol("icd10cm_code")

mapper = medical.ChunkMapperModel.pretrained("icd10cm_snomed_mapper_20260901", "en", "clinical/models")\
    .setInputCols(["icd10cm_code"])\
    .setOutputCol("mappings")\
    .setRels(["snomed_code"])

pipeline = nlp.Pipeline(stages=[documentAssembler, chunkAssembler, mapper])

data = spark.createDataFrame([["A02.20"]]).toDF("text")
result = pipeline.fit(data).transform(data)

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

val chunkAssembler = new Doc2Chunk()
    .setInputCols(Array("document"))
    .setOutputCol("icd10cm_code")

val mapper = ChunkMapperModel.pretrained("icd10cm_snomed_mapper_20260901", "en", "clinical/models")
    .setInputCols(Array("icd10cm_code"))
    .setOutputCol("mappings")
    .setRels(Array("snomed_code"))

val pipeline = new Pipeline().setStages(Array(documentAssembler, chunkAssembler, mapper))

val data = Seq("A02.20").toDF("text")
val result = pipeline.fit(data).transform(data)

Results

| icd10cm_code   |   snomed_code | all_k_resolutions                                                                                                                 |
|:---------------|--------------:|:----------------------------------------------------------------------------------------------------------------------------------|
| A02.20         |      47375003 | 47375003:::                                                                                                                       |
| A00.0          |     240349003 | 240349003:::447282003:::63650001                                                                                                  |
| Z83.3          |     160303001 | 160303001:::160402005:::416855002:::430678008:::430679000:::444094009:::444161008:::704144000:::719761003:::719763000:::721151003 |

Model Information

Model Name: icd10cm_snomed_mapper_20260901
Compatibility: Healthcare NLP 6.4.1+
License: Licensed
Edition: Official
Input Labels: [icd10cm_code]
Output Labels: [mappings]
Language: en
Size: 1.3 MB