Description
This model maps NCI Thesaurus codes to UMLS codes. It is trained on the 2026AA release of the Unified Medical Language System (UMLS) dataset.
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("doc")
doc2chunk = Doc2Chunk()\
.setInputCols(["doc"])\
.setOutputCol("ner_chunk")
mapper = ChunkMapperModel.pretrained("nci_umls_mapper","en","clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")
pipeline = Pipeline(stages=[document_assembler, doc2chunk, mapper])
data = spark.createDataFrame([["C2378"],["C7057"],["C4872"]]).toDF("text")
result = pipeline.fit(data).transform(data)
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("doc")
doc2chunk = nlp.Doc2Chunk()\
.setInputCols(["doc"])\
.setOutputCol("ner_chunk")
mapper = medical.ChunkMapperModel.pretrained("nci_umls_mapper","en","clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")
pipeline = nlp.Pipeline(stages=[document_assembler, doc2chunk, mapper])
data = spark.createDataFrame([["C2378"],["C7057"],["C4872"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("doc")
val doc2chunk = new Doc2Chunk()
.setInputCols(Array("doc"))
.setOutputCol("ner_chunk")
val mapper = ChunkMapperModel.pretrained("nci_umls_mapper","en","clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("mappings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, doc2chunk, mapper))
val data = Seq("C2378","C7057","C4872").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
| nci_code | umls_code |
|:-----------|:------------|
| C2378 | C0281309 |
| C7057 | C1511989 |
| C4872 | C0678222 |
Model Information
| Model Name: | nci_umls_mapper |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 2.7 MB |