Description
This model maps clinical entities and concepts (like drugs/ingredients) to RxNorm codes using sbiobert_base_cased_mli Sentence Bert Embeddings. It 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
sbiobertresolve_rxnorm_augmented resolver model must be used with sbiobert_base_cased_mli as embeddings ner_posology as NER model. DRUG set in .setWhiteList().
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("sbert_embeddings")\
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
rxnorm_pipeline = Pipeline(stages = [
documentAssembler,
sbert_embedder,
rxnorm_resolver])
model = rxnorm_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
light_model = LightPipeline(model)
result = light_model.fullAnnotate(["Coumadin 5 mg", "aspirin", "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_augmented_cased", "en", "clinical/models")
.setInputCols("sbert_embeddings")
.setOutputCol("rxnorm_code")
.setDistanceFunction("EUCLIDEAN")
val rxnorm_pipelineModel = new PipelineModel().setStages(Array(documentAssembler,
sbert_embedder,
rxnorm_resolver))
val data = Seq(Array("Coumadin 5 mg", "aspirin", ""avandia 4 mg")).toDS.toDF("text")
val result= rxnorm_pipelineModel.fit(data).transform(data)
import nlu
nlu.load("en.resolve.rxnen.med_ner.deid_subentityorm_augmented").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:::432467:::438740:::103... | 3.0367:::4.7790:::4.7790:::5.3... | 0.0161:::0.0395:::0.0395:::0.0... | warfarin sodium 5 MG [Coumadin]:::coumarin 5 MG Oral Tablet:... | Branded Drug Comp:::Clinical D... |
| 1 | 1537020 | aspirin Effervescent Oral Tablet | 1537020:::1191:::1295740:::405... | 0.0000:::0.0000:::4.1826:::5.7... | 0.0000:::0.0000:::0.0292:::0.0... | aspirin Effervescent Oral Tablet:::aspirin:::aspirin Oral Po... | Clinical Drug Form:::Ingredien... |
| 2 | 261242 | rosiglitazone 4 MG Oral Tablet [Avandia] | 261242:::810073:::153845:::109... | 0.0000:::4.7482:::5.0125:::5.2... | 0.0000:::0.0365:::0.0409:::0.0... | rosiglitazone 4 MG Oral Tablet [Avandia]:::fesoterodine fuma... | Branded Drug:::Branded Drug Co... |
Model Information
| Model Name: | sbiobertresolve_rxnorm_augmented |
| Compatibility: | Healthcare NLP 3.3.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [rxnorm_code] |
| Language: | en |
| Size: | 976.1 MB |
| Case sensitive: | false |