Description
This pretrained model maps UMLS codes to corresponding ICD10CM 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
icd10cm_code
How to use
document_assembler = DocumentAssembler()\
.setInputCol('text')\
.setOutputCol('document')
chunk_assembler = Doc2Chunk()\
.setInputCols(['document'])\
.setOutputCol('ner_chunk')
mapperModel = ChunkMapperModel.pretrained("umls_icd10cm_mapper", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")\
.setRels(["icd10cm_code"])
mapper_pipeline = Pipeline(stages=[
document_assembler,
chunk_assembler,
mapperModel
])
data = spark.createDataFrame([["C0000744"], ["C2875181"]]).toDF("text")
result = mapper_pipeline.fit(data).transform(data)
document_assembler = nlp.DocumentAssembler()\
.setInputCol('text')\
.setOutputCol('document')
chunk_assembler = medical.Doc2Chunk()\
.setInputCols(['document'])\
.setOutputCol('ner_chunk')
mapperModel = medical.ChunkMapperModel.pretrained("umls_icd10cm_mapper", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")\
.setRels(["icd10cm_code"])
mapper_pipeline = Pipeline(stages=[
document_assembler,
chunk_assembler,
mapperModel
])
data = spark.createDataFrame([["C0000744"], ["C2875181"]]).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("ner_chunk")
val chunkerMapper = ChunkMapperModel
.pretrained("umls_icd10cm_mapper", "en", "clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("mappings")
.setRels(Array("icd10cm_code"))
val mapper_pipeline = new Pipeline().setStages(Array(
document_assembler,
chunk_assembler,
chunkerMapper))
val data = Seq("C0000744", "C2875181").toDF("text")
val result = mapper_pipeline.fit(data).transform(data)
Results
+---------+------------+------------+
|umls_code|icd10cm_code| relation|
+---------+------------+------------+
| C0000744| E78.6|icd10cm_code|
| C2875181| G43.81|icd10cm_code|
+---------+------------+------------+
Model Information
| Model Name: | umls_icd10cm_mapper |
| Compatibility: | Healthcare NLP 5.5.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 1.5 MB |
References
Trained on concepts from ICD10CM for the 2024AB release of the Unified Medical Language System® (UMLS) Knowledge Sources: https://www.nlm.nih.gov/research/umls/index.html