Description
This pretrained model maps entities (Major Clinical Concepts) with corresponding UMLS CUI 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
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])
test_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(test_data).transform(test_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("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 test_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(test_data).transform(test_data)
import nlu
nlu.load("en.map_entity.umls_major_concepts_mapper").predict("""The patient complains of pustules after falling from stairs. Also, she has a history of quadriceps tendon rupture""")
Results
+-------------------------+---------+
|ner_chunk |umls_code|
+-------------------------+---------+
|pustules |C0241157 |
|stairs |C4300351 |
|quadriceps tendon rupture|C0263968 |
+-------------------------+---------+
Model Information
| Model Name: | umls_major_concepts_mapper |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 37.0 MB |
References
Data sampled from https://www.nlm.nih.gov/research/umls/index.html