Mapping SNOMED Codes with Their Corresponding ICD10-CM Codes

Description

This model maps SNOMED codes to their corresponding ICD10-CM 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("snomed_code")

mapper = ChunkMapperModel.pretrained("snomed_icd10cm_mapper_20260901", "en", "clinical/models")\
    .setInputCols(["snomed_code"])\
    .setOutputCol("mappings")\
    .setRels(["icd10cm_code"])

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

data = spark.createDataFrame([["1001000119102"]]).toDF("text")
result = pipeline.fit(data).transform(data)
documentAssembler = nlp.DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

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

mapper = medical.ChunkMapperModel.pretrained("snomed_icd10cm_mapper_20260901", "en", "clinical/models")\
    .setInputCols(["snomed_code"])\
    .setOutputCol("mappings")\
    .setRels(["icd10cm_code"])

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

data = spark.createDataFrame([["1001000119102"]]).toDF("text")
result = pipeline.fit(data).transform(data)

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

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

val mapper = ChunkMapperModel.pretrained("snomed_icd10cm_mapper_20260901", "en", "clinical/models")
    .setInputCols(Array("snomed_code"))
    .setOutputCol("mappings")
    .setRels(Array("icd10cm_code"))

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

val data = Seq("1001000119102").toDF("text")
val result = pipeline.fit(data).transform(data)

Results

|   snomed_code | icd10cm_code   | all_k_resolutions   |
|--------------:|:---------------|:--------------------|
| 1001000119102 | I26.99         | I26.99:::           |
|      10001005 | A41.9          | A41.9:::P36.9       |
|      10000006 | R07.9          | R07.9:::            |

Model Information

Model Name: snomed_icd10cm_mapper_20260901
Compatibility: Healthcare NLP 6.4.1+
License: Licensed
Edition: Official
Input Labels: [snomed_code]
Output Labels: [mappings]
Language: en
Size: 2.3 MB