Edit on GitHub
  • John Snow Labs
  • Mar 05, 2026

Mapping UMLS Codes with Their Corresponding CPT Codes

  • licensed
  • en
  • umls
  • cpt
  • mapping

Description

This pretrained model maps UMLS codes to corresponding CPT codes.

Predicted Entities

cpt_code

How to use


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

chunk_assembler = Doc2Chunk()\
    .setInputCols(["document"])\
    .setOutputCol("umls_code")

mapperModel = ChunkMapperModel.pretrained("umls_cpt_mapper", "en", "clinical/models")\
    .setInputCols(["umls_code"])\
    .setOutputCol("mappings")

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

data = spark.createDataFrame([["C3248275"],["C3496535"],["C0973430"],["C3248301"]]).toDF("text")

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



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

chunk_assembler = nlp.Doc2Chunk()\
    .setInputCols(["document"])\
    .setOutputCol("umls_code")

mapperModel = medical.ChunkMapperModel.pretrained("umls_cpt_mapper", "en", "clinical/models")\
    .setInputCols(["umls_code"])\
    .setOutputCol("mappings")

mapper_pipeline = nlp.Pipeline(stages=[
    document_assembler,
    chunk_assembler,
    mapperModel
])

data = spark.createDataFrame([["C3248275"],["C3496535"],["C0973430"],["C3248301"]]).toDF("text")

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



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

val chunk_assembler = new Doc2Chunk()
      .setInputCols("document")
      .setOutputCol("umls_code")

val chunkerMapper = ChunkMapperModel
      .pretrained("umls_cpt_mapper", "en", "clinical/models")
      .setInputCols(Array("umls_code"))
      .setOutputCol("mappings")
      
val mapper_pipeline = Pipeline().setStages(Array(
                                                  document_assembler,
                                                  chunk_assembler,
                                                  chunkerMapper))

val data = Seq("C3248275","C3496535","C0973430","C3248301").toDF("text")

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

Results


+---------+--------+
|umls_code|cpt_code|
+---------+--------+
|C3248275 |2016F   |
|C3496535 |48155   |
|C0973430 |64823   |
|C3248301 |4500F   |
+---------+--------+

Model Information

Model Name: umls_cpt_mapper
Compatibility: Healthcare NLP 6.3.0+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 634.2 KB

References

CPT resolver models are removed from the Models Hub due to license restrictions and can only be shared with the users who already have a valid CPT license. If you possess one and wish to use this model, kindly contact us at support@johnsnowlabs.com.