Description
This pretrained model maps UMLS codes to corresponding SNOMED codes.
Predicted Entities
snomed_code
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("sbert_embeddings")\
.setCaseSensitive(False)
umls_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_umls_clinical_drugs", "en", "clinical/models")\
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("umls_code")\
.setDistanceFunction("EUCLIDEAN")
resolver2chunk = Resolution2Chunk()\
.setInputCols(["umls_code"])\
.setOutputCol("umls2chunk")
chunkerMapper = ChunkMapperModel.pretrained("umls_snomed_mapper", "en", "clinical/models")\
.setInputCols(["umls2chunk"])\
.setOutputCol("mappings")\
.setRels(["snomed_code"])
mapper_pipeline = Pipeline(stages = [
documentAssembler,
sbert_embedder,
umls_resolver,
resolver2chunk,
chunkerMapper])
data = spark.createDataFrame([["acebutolol"],["aspirin"]]).toDF("text")
result = mapper_pipeline.fit(data).transform(data)
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
sbert_embedder = nlp.BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("sbert_embeddings")\
.setCaseSensitive(False)
umls_resolver = medical.SentenceEntityResolverModel.pretrained("sbiobertresolve_umls_clinical_drugs", "en", "clinical/models")\
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("umls_code")\
.setDistanceFunction("EUCLIDEAN")
resolver2chunk = medical.Resolution2Chunk()\
.setInputCols(["umls_code"])\
.setOutputCol("umls2chunk")
chunkerMapper = medical.ChunkMapperModel.pretrained("umls_snomed_mapper", "en", "clinical/models")\
.setInputCols(["umls2chunk"])\
.setOutputCol("mappings")\
.setRels(["snomed_code"])
mapper_pipeline = nlp.Pipeline(stages = [
documentAssembler,
sbert_embedder,
umls_resolver,
resolver2chunk,
chunkerMapper])
data = spark.createDataFrame([["acebutolol"],["aspirin"]]).toDF("text")
result = mapper_pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en","clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("sbert_embeddings")
.setCaseSensitive(false)
val umls_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_umls_clinical_drugs","en","clinical/models")
.setInputCols(Array("sbert_embeddings"))
.setOutputCol("umls_code")
.setDistanceFunction("EUCLIDEAN")
val resolver2chunk = new Resolution2Chunk()
.setInputCols(Array("umls_code"))
.setOutputCol("umls2chunk")
val chunkerMapper = ChunkMapperModel.pretrained("umls_snomed_mapper","en","clinical/models")
.setInputCols(Array("umls2chunk"))
.setOutputCol("mappings")
.setRels(["snomed_code"])
val mapper_pipeline = new Pipeline().setStages(Array(
documentAssembler,
sbert_embedder,
umls_resolver,
resolver2chunk,
chunkerMapper))
val data = Seq("acebutolol", "aspirin").toDF("text")
val result = mapper_pipeline.fit(data).transform(data)
Results
+----------+---------+-----------+
|chunk |umls_code|snomed_code|
+----------+---------+-----------+
|acebutolol|C3208139 |767585002 |
|aspirin |C0732305 |358427004 |
+----------+---------+-----------+
Model Information
| Model Name: | umls_snomed_mapper |
| Compatibility: | Healthcare NLP 5.5.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 7.1 MB |
References
Trained on concepts from SNOMED for the 2024AB release of the Unified Medical Language System® (UMLS) Knowledge Sources: https://www.nlm.nih.gov/research/umls/index.html