Mapping RxNorm Codes with Corresponding National Drug Codes

Description

This pretrained model maps RxNorm and RxNorm Extension codes with corresponding National Drug Codes (NDC).

Predicted Entities

Product NDC, Package NDC

Open in Colab Download

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(["ner_chunk", "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("ner_chunk", "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|