Description
This pretrained model maps RXNORM codes to corresponding UMLS codes.
Predicted Entities
umls_code
Open in Colab Copy S3 URICopied!
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")
rxnorm_resolver = SentenceEntityResolverModel\
.pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models")\
.setInputCols(["ner_chunk", "sbert_embeddings"])\
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
chunkerMapper = ChunkMapperModel\
.pretrained("rxnorm_umls_mapper", "en", "clinical/models")\
.setInputCols(["rxnorm_code"])\
.setOutputCol("umls_mappings")\
.setRels(["umls_code"])
pipeline = Pipeline(stages = [
documentAssembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper
])
model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
light_pipeline= LightPipeline(model)
result = light_pipeline.fullAnnotate("amlodipine 5 MG")
Results
| | ner_chunk | rxnorm_code | umls_mappings |
|---:|:----------------|--------------:|:----------------|
| 0 | amlodipine 5 MG | 329528 | C1124796 |
Model Information
| Model Name: | rxnorm_umls_mapper |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [rxnorm_code] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 1.9 MB |
References
This pretrained model maps RXNORM codes to corresponding UMLS codes under the Unified Medical Language System (UMLS).