Description
This pretrained model maps RxNorm and RxNorm Extension codes with corresponding National Drug Codes (NDC).
Predicted Entities
Product NDC, Package NDC
How to use
document_assembler = DocumentAssembler()\
.setInputCol('text')\
.setOutputCol('ner_chunk')
sbert_embedder = BertSentenceEmbeddings.pretrained('sbiobert_base_cased_mli', 'en','clinical/models')\
.setInputCols(["ner_chunk"])\
.setOutputCol("sentence_embeddings")\
.setCaseSensitive(False)
rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_augmented","en", "clinical/models") \
.setInputCols(["sentence_embeddings"]) \
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
chunkerMapper_product = ChunkMapperModel.pretrained("rxnorm_ndc_mapper", "en", "clinical/models")\
.setInputCols(["rxnorm_code"])\
.setOutputCol("Product NDC")\
.setRel("Product NDC")
chunkerMapper_package = ChunkMapperModel.pretrained("rxnorm_ndc_mapper", "en", "clinical/models")\
.setInputCols(["rxnorm_code"])\
.setOutputCol("Package NDC")\
.setRel("Package NDC")
pipeline = Pipeline().setStages([document_assembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper_product,
chunkerMapper_package
])
model = pipeline.fit(spark.createDataFrame([['']]).toDF('text'))
lp = LightPipeline(model)
result = lp.annotate(['doxepin hydrochloride 50 MG/ML', 'macadamia nut 100 MG/ML'])
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en","clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("sentence_embeddings")
.setCaseSensitive(False)
val rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_augmented","en", "clinical/models")
.setInputCols(Array("sentence_embeddings"))
.setOutputCol("rxnorm_code")
.setDistanceFunction("EUCLIDEAN")
val chunkerMapper_product = ChunkMapperModel.pretrained("rxnorm_ndc_mapper", "en", "clinical/models")
.setInputCols(Array("rxnorm_code"))
.setOutputCol("Product NDC")
.setRel("Product NDC")
val chunkerMapper_package = ChunkMapperModel.pretrained("rxnorm_ndc_mapper", "en", "clinical/models")
.setInputCols(Array("rxnorm_code"))
.setOutputCol("Package NDC")
.setRel("Package NDC")
val pipeline = new Pipeline().setStages(Array(
document_assembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper_product,
chunkerMapper_package
))
val text_data = Seq("doxepin hydrochloride 50 MG/ML", "macadamia nut 100 MG/ML").toDS.toDF("text")
val res = pipeline.fit(text_data).transform(text_data)
import nlu
nlu.load("en.rxnorm_to_ndc").predict("""Product NDC""")
Results
| | ner_chunk | rxnorm_code | Package NDC | Product NDC |
|---:|:-----------------------------------|:--------------|:------------------|:---------------|
| 0 | ['doxepin hydrochloride 50 MG/ML'] | ['1000091'] | ['00378-8117-45'] | ['00378-8117'] |
| 1 | ['macadamia nut 100 MG/ML'] | ['212433'] | ['00064-2120-08'] | ['00064-2120'] |
Model Information
|—|—| |Model Name:|rxnorm_ndc_mapper| |Compatibility:|Healthcare NLP 3.5.1+| |License:|Licensed| |Edition:|Official| |Input Labels:|[chunk]| |Output Labels:|[mappings]| |Language:|en| |Size:|4.2 MB|