Description
This pretrained model maps RxNorm and RxNorm Extension codes with their corresponding action and treatment. Action refers to the function of the drug in various body systems; treatment refers to which disease the drug is used to treat.
Predicted Entities
action, treatment
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_action = ChunkMapperModel.pretrained("rxnorm_action_treatment_mapper", "en", "clinical/models")\
.setInputCols(["rxnorm_code"])\
.setOutputCol("Action")\
.setRel("Action")
chunkerMapper_treatment = ChunkMapperModel.pretrained("rxnorm_action_treatment_mapper", "en", "clinical/models")\
.setInputCols(["rxnorm_code"])\
.setOutputCol("Treatment")\
.setRel("Treatment")
pipeline = Pipeline().setStages([document_assembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper_action,
chunkerMapper_treatment
])
model = pipeline.fit(spark.createDataFrame([['']]).toDF('text'))
light_pipeline = LightPipeline(model)
result = light_pipeline.annotate(['Sinequan 150 MG', 'Zonalon 50 mg'])
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_action = ChunkMapperModel.pretrained("rxnorm_action_treatment_mapper", "en", "clinical/models"))
.setInputCols("rxnorm_code")
.setOutputCol("Action")
.setRel("Action")
val chunkerMapper_treatment = ChunkMapperModel.pretrained("rxnorm_action_treatment_mapper", "en", "clinical/models"))
.setInputCols("rxnorm_code")
.setOutputCol("Treatment")
.setRel("Treatment")
val pipeline = new Pipeline().setStages(Array(document_assembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper_action,
chunkerMapper_treatment
))
val text_data = Seq("Sinequan 150 MG", "Zonalon 50 mg").toDS.toDF("text")
val res = pipeline.fit(text_data).transform(text_data)
import nlu
nlu.load("en.map_entity.rxnorm_to_action_treatment").predict("""Sinequan 150 MG""")
Results
| | ner_chunk | rxnorm_code | Treatment | Action |
|---:|:--------------------|:--------------|:-------------------------------------------------------------------------------|:-----------------------------------------------------------------------|
| 0 | ['Sinequan 150 MG'] | ['1000067'] | ['Alcoholism', 'Depression', 'Neurosis', 'Anxiety&Panic Attacks', 'Psychosis'] | ['Antidepressant', 'Anxiolytic', 'Psychoanaleptics', 'Sedative'] |
| 1 | ['Zonalon 50 mg'] | ['103971'] | ['Pain'] | ['Analgesic', 'Analgesic (Opioid)', 'Analgetic', 'Opioid', 'Vitamins'] |
Model Information
|—|—| |Model Name:|rxnorm_action_treatment_mapper| |Compatibility:|Healthcare NLP 3.5.1+| |License:|Licensed| |Edition:|Official| |Input Labels:|[chunk]| |Output Labels:|[mappings]| |Language:|en| |Size:|19.3 MB|