Description
This model maps clinical entities and concepts (like drugs/ingredients) to RxNorm codes using sbert_jsl_medium_rxnorm_uncased Sentence Bert Embeddings. It is trained on the augmented version of the dataset which is used in previous RxNorm resolver models. Additionally, this model returns concept classes of the drugs in all_k_aux_labels column.
Predicted Entities
RxNorm Codes, Concept Classes
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
sbert_embedder = BertSentenceEmbeddings.pretrained('sbert_jsl_medium_rxnorm_uncased', 'en','clinical/models')\
.setInputCols(["ner_chunk"])\
.setOutputCol("sbert_embeddings")
rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_jsl_rxnorm_augmented_med", "en", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
rxnorm_pipelineModel = PipelineModel(
stages = [
documentAssembler,
sbert_embedder,
rxnorm_resolver])
light_model = LightPipeline(rxnorm_pipelineModel)
result = light_model.fullAnnotate(["Coumadin 5 mg", "aspirin", "Neurontin 300", "avandia 4 mg"])
val documentAssembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings.pretrained("sbert_jsl_medium_rxnorm_uncased", "en", "clinical/models")
.setInputCols("ner_chunk")
.setOutputCol("sbert_embeddings")
val rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_jsl_rxnorm_augmented_med", "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("Coumadin 5 mg", "aspirin", "Neurontin 300", "avandia 4 mg"))
import nlu
nlu.load("en.resolve.rxnorm.augmented_med").predict("""Coumadin 5 mg""")
Results
| | RxNormCode | Resolution | all_k_results | all_k_distances | all_k_cosine_distances | all_k_resolutions | all_k_aux_labels |
|---:|-------------:|:-------------------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------------------------------------|:----------------------------------|
| 0 | 855333 | warfarin sodium 5 MG [Coumadin] | 855333:::855334:::1110792:::11... | 0.0000:::6.0548:::6.1667:::6.1... | 0.0000:::0.0515:::0.0536:::0.0... | warfarin sodium 5 MG [Coumadin]:::warfarin sodium 5 MG Oral ... | Branded Drug Comp:::Branded Dr... |
| 1 | 1537020 | aspirin Effervescent Oral Tablet | 1537020:::1191:::202547:::1001... | 0.0000:::0.0000:::8.8123:::9.3... | 0.0000:::0.0000:::0.1145:::0.1... | aspirin Effervescent Oral Tablet:::aspirin:::Empirin:::Ecpir... | Clinical Drug Form:::Ingredien... |
| 2 | 105029 | gabapentin 300 MG Oral Capsule [Neurontin] | 105029:::1718929:::1718930:::3... | 5.5969:::8.7502:::8.7502:::8.7... | 0.0452:::0.1092:::0.1092:::0.1... | gabapentin 300 MG Oral Capsule [Neurontin]:::olanzapine 300 ... | Branded Drug:::Clinical Drug C... |
| 3 | 261242 | rosiglitazone 4 MG Oral Tablet [Avandia] | 261242:::2123140:::1792373:::8... | 0.0000:::7.1217:::7.7113:::8.4... | 0.0000:::0.0728:::0.0843:::0.1... | rosiglitazone 4 MG Oral Tablet [Avandia]:::erdafitinib 4 MG ... | Branded Drug:::Branded Drug Co... |
Model Information
| Model Name: | sbertresolve_jsl_rxnorm_augmented_med |
| Compatibility: | Healthcare NLP 3.3.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [rxnorm_code] |
| Language: | en |
| Size: | 650.7 MB |
| Case sensitive: | false |