Description
This model maps medication entities (like drugs/ingredients) to RxNorm codes and their dispositions using sbert_jsl_medium_uncased Sentence Bert Embeddings. If you look for a faster inference with just drug names (excluding dosage and strength), this version of RxNorm model would be a better alternative.
Predicted Entities
Predicts RxNorm Codes, their normalized definition for each chunk, and dispositions if any. In the result, look for the aux_label parameter in the metadata to get dispositions divided by |.
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
sbert_embedder = BertSentenceEmbeddings.pretrained('sbert_jsl_medium_uncased', 'en','clinical/models')\
.setInputCols(["ner_chunk"])\
.setOutputCol("sbert_embeddings")
rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_rxnorm_disposition", "en", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
rxnorm_pipelineModel = PipelineModel(
stages = [
documentAssembler,
sbert_embedder,
rxnorm_resolver])
rxnorm_lp = LightPipeline(rxnorm_pipelineModel)
result = rxnorm_lp.fullAnnotate("alizapride 25 mg/ml")
val documentAssembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings.pretrained("sbert_jsl_medium_uncased", "en","clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("sbert_embeddings")
val rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_rxnorm_disposition", "en", "clinical/models")
.setInputCols(Array("sbert_embeddings"))
.setOutputCol("rxnorm_code")
.setDistanceFunction("EUCLIDEAN")
val pipelineModel= new PipelineModel().setStages(Array(documentAssembler, sbert_embedder, rxnorm_resolver))
val rxnorm_lp = LightPipeline(pipelineModel)
val result = rxnorm_lp.fullAnnotate("alizapride 25 mg/ml")
import nlu
nlu.load("en.resolve.rxnorm.disposition").predict("""alizapride 25 mg/ml""")
Results
| | chunks | code | resolutions | all_codes | all_k_aux_labels | all_distances |
|---:|:-------------------|:-------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|
| 0 |alizapride 25 mg/ml | 330948 | [alizapride 25 mg/ml, alizapride 50 mg, alizapride 25 mg/ml oral solution, adalimumab 50 mg/ml, adalimumab 100 mg/ml [humira], adalimumab 50 mg/ml [humira], alirocumab 150 mg/ml, ...]| [330948, 330949, 249531, 358817, 1726845, 576023, 1659153, ...] | [Dopamine receptor antagonist, Dopamine receptor antagonist, Dopamine receptor antagonist, -, -, -, -, ...] | [0.0000, 0.0936, 0.1166, 0.1525, 0.1584, 0.1567, 0.1631, ...] |
Model Information
| Model Name: | sbertresolve_rxnorm_disposition |
| Compatibility: | Healthcare NLP 3.1.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk, sbert_embeddings] |
| Output Labels: | [new_code] |
| Language: | en |
| Case sensitive: | false |