Description
This model maps clinical entities and concepts (like drugs/ingredients) to RxNorm codes using sbiobert_base_cased_mli Sentence Bert Embeddings. Additionally, this model returns actions and treatments of the drugs in all_k_aux_labels column.
Predicted Entities
RxNorm Codes, 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_action_treatment", "en", "clinical/models")\
.setInputCols(["sbert_embeddings"])\
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
pipelineModel = PipelineModel( stages = [
documentAssembler,
sbert_embedder,
rxnorm_resolver ])
light_model = LightPipeline(pipelineModel)
result = light_model.fullAnnotate(["Zita 200 mg", "coumadin 5 mg", "avandia 4 mg"])
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en","clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("sbert_embeddings")
val rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_action_treatment", "en", "clinical/models")
.setInputCols(Array("sbert_embeddings"))
.setOutputCol("rxnorm_code")
.setDistanceFunction("EUCLIDEAN")
val rxnorm_pipelineModel = new PipelineModel().setStages(Array(
documentAssembler,
sbert_embedder,
rxnorm_resolver))
val light_model = LightPipeline(rxnorm_pipelineModel)
val result = light_model.fullAnnotate(Array("Zita 200 mg", "coumadin 5 mg", "avandia 4 mg"))
import nlu
nlu.load("en.resolve.rxnorm_action_treatment").predict("""coumadin 5 mg""")
Results
| | ner_chunk | rxnorm_code | action | treatment |
|---:|:--------------|--------------:|:---------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | Zita 200 mg | 104080 | ['Analgesic', 'Antacid', 'Antipyretic', 'Pain Reliever'] | ['Backache', 'Pain', 'Sore Throat', 'Headache', 'Influenza', 'Toothache', 'Heartburn', 'Migraine', 'Muscular Aches And Pains', 'Neuralgia', 'Cold', 'Weakness'] |
| 1 | coumadin 5 mg | 855333 | ['Anticoagulant'] | ['Cerebrovascular Accident', 'Pulmonary Embolism', 'Heart Attack', 'AF', 'Embolization'] |
| 2 | avandia 4 mg | 261242 | ['Drugs Used In Diabets', 'Hypoglycemic'] | ['Diabetes Mellitus', 'Type 1 Diabetes Mellitus', 'Type 2 Diabetes'] |
Model Information
| Model Name: | sbiobertresolve_rxnorm_action_treatment |
| Compatibility: | Healthcare NLP 3.5.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [rxnorm_code] |
| Language: | en |
| Size: | 918.7 MB |
| Case sensitive: | false |