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  • John Snow Labs
  • Jun 12, 2026

Mapping Entities (General Concepts) with Corresponding UMLS CUI Codes

  • en
  • chunk_mapper
  • licensed
  • clinical
  • umls
  • general

Description

This model maps general biomedical concept entities extracted by NER to UMLS CUI codes covering Disease, Symptom, Device, and Procedure semantic types (T047, T184, T074, T061), comprising approximately 1,170,000 name–CUI pairs. 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("document")

sentence_detector = SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("embeddings")

ner_model = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("clinical_ner")

ner_model_converter = NerConverterInternal()\
    .setInputCols(["sentence", "token", "clinical_ner"])\
    .setOutputCol("ner_chunk")

chunkerMapper = ChunkMapperModel.pretrained("umls_general_concepts_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["umls_code"])\
    .setLowerCase(True)

mapper_pipeline = Pipeline(stages=[
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_model,
    ner_model_converter,
    chunkerMapper,
])
data = spark.createDataFrame([["The patient presents with dyspnea and fever due to pneumonia. Treatment includes bronchoscopy, catheter placement, and chemotherapy."]]).toDF("text")
result = mapper_pipeline.fit(data).transform(data)



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

sentence_detector = nlp.SentenceDetector()\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = nlp.Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

word_embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token"])\
    .setOutputCol("embeddings")

ner_model = medical.NerModel.pretrained("ner_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("clinical_ner")

ner_model_converter = medical.NerConverterInternal()\
    .setInputCols(["sentence", "token", "clinical_ner"])\
    .setOutputCol("ner_chunk")

chunkerMapper = medical.ChunkMapperModel.pretrained("umls_general_concepts_mapper", "en", "clinical/models")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("mappings")\
    .setRels(["umls_code"])\
    .setLowerCase(True)

mapper_pipeline = nlp.Pipeline(stages=[
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_model,
    ner_model_converter,
    chunkerMapper,
])
data = spark.createDataFrame([["The patient presents with dyspnea and fever due to pneumonia. Treatment includes bronchoscopy, catheter placement, and chemotherapy."]]).toDF("text")
result = mapper_pipeline.fit(data).transform(data)



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

val sentenceDetector = new SentenceDetector()
    .setInputCols(Array("document"))
    .setOutputCol("sentence")

val tokenizer = new Tokenizer()
    .setInputCols(Array("sentence"))
    .setOutputCol("token")

val wordEmbeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("embeddings")

val nerModel = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "embeddings"))
    .setOutputCol("clinical_ner")

val nerModelConverter = new NerConverterInternal()
    .setInputCols(Array("sentence", "token", "clinical_ner"))
    .setOutputCol("ner_chunk")

val chunkerMapper = ChunkMapperModel.pretrained("umls_general_concepts_mapper", "en", "clinical/models")
    .setInputCols(Array("ner_chunk"))
    .setOutputCol("mappings")
    .setRels(Array("umls_code"))
    .setLowerCase(true)

val mapperPipeline = new Pipeline().setStages(Array(
    documentAssembler,
    sentenceDetector,
    tokenizer,
    wordEmbeddings,
    nerModel,
    nerModelConverter,
    chunkerMapper,
))

val data = Seq("The patient presents with dyspnea and fever due to pneumonia. Treatment includes bronchoscopy, catheter placement, and chemotherapy.").toDF("text")
val result = mapperPipeline.fit(data).transform(data)

Results

| ner_chunk          | umls_code   |
|:-------------------|:------------|
| dyspnea            | C0013404    |
| fever              | C0015967    |
| pneumonia          | C0032285    |
| bronchoscopy       | C5979970    |
| catheter placement | C0883301    |
| chemotherapy       | C0013216    |

Model Information

Model Name: umls_general_concepts_mapper
Compatibility: Healthcare NLP 6.4.0+
License: Licensed
Edition: Official
Input Labels: [ner_chunk]
Output Labels: [mappings]
Language: en
Size: 46.5 MB