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 = nlp.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(['doxycycline hyclate 50 MG Oral Tablet', 'macadamia nut 100 MG/ML'])
val document_assembler = 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 = Pipeline().setStages(Array(document_assembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper_product,
chunkerMapper_package
))
val text_data = Seq("doxycycline hyclate 50 MG Oral Tablet'", "macadamia nut 100 MG/ML").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 | ['doxycycline hyclate 50 MG Oral Tablet'] | ['1652674'] | ['62135-0625-60'] | ['62135-0625'] |
| 1 | ['macadamia nut 100 MG/ML'] | ['212433'] | ['00187-1474-08'] | ['00187-1474'] |
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: | 2.0 MB |