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
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_ndc_mapper", "en", "clinical/models")\
.setInputCols(["rxnorm_code"])\
.setOutputCol("ndc_mappings")\
.setRels(["Product NDC", "Package NDC"])
pipeline = Pipeline(stages = [
documentAssembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper
])
model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
light_pipeline = LightPipeline(model)
result = light_pipeline.annotate(["doxycycline hyclate 50 MG Oral Tablet", "macadamia nut 100 MG/ML"])
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings
.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
.setInputCols("ner_chunk")
.setOutputCol("sbert_embeddings")
val rxnorm_resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_rxnorm_augmented", "en", "clinical/models")
.setInputCols(Array("ner_chunk", "sbert_embeddings"))
.setOutputCol("rxnorm_code")
.setDistanceFunction("EUCLIDEAN")
val chunkerMapper = ChunkMapperModel
.pretrained("rxnorm_ndc_mapper", "en", "clinical/models")
.setInputCols("rxnorm_code")
.setOutputCol("ndc_mappings")
.setRels("Product NDC", "Package NDC")
val pipeline = new Pipeline(stages = Array(
documentAssembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper
))
val data = Seq(Array("doxycycline hyclate 50 MG Oral Tablet", "macadamia nut 100 MG/ML")).toDS.toDF("text")
val result= pipeline.fit(data).transform(data)
import nlu
nlu.load("en.rxnorm_to_ndc").predict("""doxycycline hyclate 50 MG Oral Tablet""")
Results
| | ner_chunk | rxnorm_code | Package NDC | Product NDC |
|---:|:--------------------------------------|--------------:|:--------------|:--------------|
| 0 | doxycycline hyclate 50 MG Oral Tablet | 1652674 | 62135-0625-60 | 46708-0499 |
| 1 | macadamia nut 100 MG/ML | 259934 | 13349-0010-39 | 13349-0010 |
Model Information
| Model Name: | rxnorm_ndc_mapper |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 2.0 MB |