Description
This model maps medication entities (like drugs/ingredients) to RxNorm codes and their dispositions using sbiobert_base_cased_mli Sentence Bert Embeddings.
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 |.
Live Demo Open in Colab Copy S3 URI
How to use
sbiobertresolve_rxnorm_disposition 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_disposition", "en", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("rxnorm_code")\
.setDistanceFunction("EUCLIDEAN")
pipelineModel = PipelineModel(stages = [
documentAssembler,
sbert_embedder,
rxnorm_resolver
])
rxnorm_lp = LightPipeline(pipelineModel)
result = rxnorm_lp.fullAnnotate("belimumab 80 mg/ml injectable solution")
val documentAssembler = 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_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("belimumab 80 mg/ml injectable solution")
import nlu
nlu.load("en.resolve.rxnorm_disposition").predict("""belimumab 80 mg/ml injectable solution""")
Results
| | chunks | code | resolutions | all_codes | all_k_aux_labels | all_distances |
|---:|:--------------------------------------|:--------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|:--------------------------------------------------------------------------------------------|:----------------------------------------------|
| 0 |belimumab 80 mg/ml injectable solution | 1092440 | [belimumab 80 mg/ml injectable solution, belimumab 80 mg/ml injectable solution [benlysta], ifosfamide 80 mg/ml injectable solution, belimumab 80 mg/ml [benlysta], belimumab 80 mg/ml, ...]| [1092440, 1092444, 107034, 1092442, 1092438, ...] | [Immunomodulator, Immunomodulator, Alkylating agent, Immunomodulator, Immunomodulator, ...] | [0.0000, 0.0145, 0.0479, 0.0619, 0.0636, ...] |
Model Information
| Model Name: | sbiobertresolve_rxnorm_disposition |
| Compatibility: | Healthcare NLP 3.1.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [rxnorm_code] |
| Language: | en |
| Case sensitive: | false |