Mapping Entities (Major Clinical Concepts) with Corresponding UMLS CUI Codes

Description

This pretrained model maps entities (Major Clinical Concepts) with corresponding UMLS CUI codes.

Predicted Entities

umls_code

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_medmentions_coarse", "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_major_concepts_mapper", "en", "clinical/models")\
      .setInputCols(["ner_chunk"])\
      .setOutputCol("mappings")\
      .setRels(["umls_code"])\
      .setLowerCase(True)

mapper_pipeline = Pipeline().setStages([
        document_assembler,
        sentence_detector,
        tokenizer,
        word_embeddings,
        ner_model,
        ner_model_converter,
        chunkerMapper])

data = spark.createDataFrame([["""The patient complains of pustules after falling from stairs. Also,  she has a history of quadriceps tendon rupture."""]]).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_medmentions_coarse", "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_major_concepts_mapper", "en", "clinical/models")\
      .setInputCols(["ner_chunk"])\
      .setOutputCol("mappings")\
      .setRels(["umls_code"])\
      .setLowerCase(True)

mapper_pipeline = Pipeline().setStages([
        document_assembler,
        sentence_detector,
        tokenizer,
        word_embeddings,
        ner_model,
        ner_model_converter,
        chunkerMapper])

data = spark.createDataFrame([["""The patient complains of pustules after falling from stairs. Also,  she has a history of quadriceps tendon rupture."""]]).toDF("text")

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

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

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

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

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

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

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

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

val mapper_pipeline = new Pipeline().setStages(Array(
                                                  document_assembler,
                                                  sentence_detector,
                                                  tokenizer,
                                                  word_embeddings,
                                                  ner_model,
                                                  ner_model_converter,
                                                  chunkerMapper))

val data = Seq("""The patient complains of pustules after falling from stairs. Also,  she has a history of quadriceps tendon rupture.""").toDF("text")

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

Results

+-------------------------+---------+
|ner_chunk                |umls_code|
+-------------------------+---------+
|pustules                 |C0241157 |
|stairs                   |C4300351 |
|quadriceps tendon rupture|C0263968 |
+-------------------------+---------+

Model Information

Model Name:	umls_major_concepts_mapper
Compatibility:	Healthcare NLP 6.0.2+
License:	Licensed
Edition:	Official
Input Labels:	[ner_chunk]
Output Labels:	[mappings]
Language:	en
Size:	54.0 MB

References

Trained on concepts from major for the 2025AA release of the Unified Medical Language System® (UMLS) Knowledge Sources: https://www.nlm.nih.gov/research/umls/index.html

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