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
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_1 = ChunkMapperModel\
.pretrained("rxnorm_action_treatment_mapper", "en", "clinical/models")\
.setInputCols(["rxnorm_code"])\
.setOutputCol("action_mappings")\
.setRels(["action"])
chunkerMapper_2 = ChunkMapperModel\
.pretrained("rxnorm_action_treatment_mapper", "en", "clinical/models")\
.setInputCols(["rxnorm_code"])\
.setOutputCol("treatment_mappings")\
.setRels(["treatment"])
pipeline = Pipeline(stages = [
documentAssembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper_1,
chunkerMapper_2
])
model = pipeline.fit(spark.createDataFrame([['']]).toDF('text'))
pipeline = LightPipeline(model)
result = pipeline.fullAnnotate(['Sinequan 150 MG', 'Zonalon 50 mg'])
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_1 = ChunkMapperModel
.pretrained("rxnorm_action_treatment_mapper", "en", "clinical/models")
.setInputCols("rxnorm_code")
.setOutputCol("action_mappings")
.setRels("action")
val chunkerMapper_2 = ChunkMapperModel
.pretrained("rxnorm_action_treatment_mapper", "en", "clinical/models")
.setInputCols("rxnorm_code")
.setOutputCol("treatment_mappings")
.setRels("treatment")
val pipeline = new Pipeline(stages = Array(
documentAssembler,
sbert_embedder,
rxnorm_resolver,
chunkerMapper_1,
chunkerMapper_2
))
val data = Seq(Array("Sinequan 150 MG", "Zonalon 50 mg")).toDS.toDF("text")
val result= pipeline.fit(data).transform(data)
import nlu
nlu.load("en.map_entity.rxnorm_to_action_treatment").predict("""Sinequan 150 MG""")
Results
| | ner_chunk | rxnorm_code | Action | Treatment |
|---:|:--------------------|:--------------|:----------------------------------------------------------|:-----------------------------------------------------------------------|
| 0 | ['Sinequan 150 MG'] | ['1000067'] | ['Anxiolytic', 'Psychoanaleptics', 'Sedative'] | ['Depression', 'Neurosis', 'Anxiety&Panic Attacks', 'Psychosis'] |
| 1 | ['Zonalon 50 mg'] | ['103971'] | ['Analgesic (Opioid)', 'Analgetic', 'Opioid', 'Vitamins'] | ['Pain'] |
Model Information
| Model Name: | rxnorm_action_treatment_mapper |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [rxnorm_code] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 21.1 MB |