Description
This model maps clinical entities (NER labels) to the most appropriate NER model using sbert_jsl_medium_uncased Sentence Bert Embeddings. Given the entity name, it will return a list of pretrained NER models having that entity or similar ones.
Predicted Entities
NER Model Names
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")
ner_model_finder = SentenceEntityResolverModel\
.pretrained("sbertresolve_ner_model_finder", "en", "clinical/models")\
.setInputCols(["ner_chunk", "sbert_embeddings"])\
.setOutputCol("model_names")\
.setDistanceFunction("EUCLIDEAN")
ner_model_finder_pipelineModel = PipelineModel(stages = [documentAssembler, sbert_embedder, ner_model_finder])
light_pipeline = LightPipeline(ner_model_finder_pipelineModel)
annotations = light_pipeline.fullAnnotate("medication")
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 ner_model_finder = SentenceEntityResolverModel
.pretrained("sbertresolve_ner_model_finder", "en", "clinical/models")
.setInputCols(Array("ner_chunk", "sbert_embeddings"))
.setOutputCol("model_names")
.setDistanceFunction("EUCLIDEAN")
val ner_model_finder_pipelineModel = new PipelineModel().setStages(Array(documentAssembler, sbert_embedder, ner_model_finder))
val light_pipeline = LightPipeline(ner_model_finder_pipelineModel)
val annotations = light_pipeline.fullAnnotate("medication")
import nlu
nlu.load("en.resolve.ner.model_finder").predict("""Put your text here.""")
Results
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|entity |models |all_models |resolutions |
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|medication|['ner_posology', 'ner_posology_large', 'ner_posology_small', 'ner_posology_greedy', 'ner_drugs_large', 'ner_posology_experimental', 'ner_drugs_greedy', 'ner_ade_clinical', 'ner_jsl_slim', 'ner_posology_healthcare', 'ner_ade_healthcare', 'jsl_ner_wip_modifier_clinical', 'ner_ade_clinical', 'ner_jsl_greedy', 'ner_risk_factors']|['ner_posology', 'ner_posology_large', 'ner_posology_small', 'ner_posology_greedy', 'ner_drugs_large', 'ner_posology_experimental', 'ner_drugs_greedy', 'ner_ade_clinical', 'ner_jsl_slim', 'ner_posology_healthcare', 'ner_ade_healthcare', 'jsl_ner_wip_modifier_clinical', 'ner_ade_clinical', 'ner_jsl_greedy', 'ner_risk_factors']:::['ner_posology', 'ner_posology_large', 'ner_posology_small', 'ner_posology_greedy', 'ner_drugs_large', 'ner_posology_experimental', 'ner_drugs_greedy', 'ner_jsl_slim', 'ner_posology_healthcare', 'ner_ade_healthcare', 'jsl_ner_wip_modifier_clinical', 'ner_ade_clinical', 'ner_jsl_greedy', 'ner_risk_factors']:::['jsl_rd_ner_wip_greedy_clinical', 'ner_clinical_large', 'ner_healthcare', 'ner_jsl_enriched', 'ner_clinical', 'ner_jsl_slim', 'ner_covid_trials', 'ner_jsl', 'jsl_ner_wip_modifier_clinical', 'ner_events_admission_clinical', 'ner_events_healthcare', 'ner_events_clinical', 'ner_jsl_greedy']:::['ner_medmentions_coarse']:::['ner_jsl_enriched', 'ner_covid_trials', 'ner_jsl', 'ner_medmentions_coarse']:::['ner_drugs']:::['ner_clinical_icdem', 'ner_medmentions_coarse']:::['jsl_rd_ner_wip_greedy_clinical', 'ner_jsl_enriched', 'ner_medmentions_coarse', 'ner_jsl', 'jsl_ner_wip_modifier_clinical', 'ner_jsl_greedy']:::['jsl_rd_ner_wip_greedy_clinical', 'ner_jsl_enriched', 'ner_medmentions_coarse', 'ner_radiology_wip_clinical', 'ner_jsl_slim', 'ner_jsl', 'jsl_ner_wip_modifier_clinical', 'ner_jsl_greedy', 'ner_radiology']:::['ner_medmentions_coarse','ner_clinical_icdem']:::['ner_posology_experimental']:::['jsl_rd_ner_wip_greedy_clinical', 'ner_measurements_clinical', 'ner_radiology_wip_clinical', 'ner_radiology']:::['jsl_rd_ner_wip_greedy_clinical', 'ner_posology_small', 'ner_jsl_enriched', 'ner_posology_experimental', 'ner_posology_large', 'ner_posology_healthcare', 'ner_jsl', 'jsl_ner_wip_modifier_clinical', 'ner_posology_greedy', 'ner_posology', 'ner_jsl_greedy']:::['ner_covid_trials', 'ner_medmentions_coarse', 'jsl_rd_ner_wip_greedy_clinical', 'ner_jsl_enriched', 'ner_jsl', 'jsl_ner_wip_modifier_clinical', 'ner_jsl_greedy']:::['ner_deid_subentity_augmented', 'ner_deid_subentity_glove', 'ner_deidentify_dl', 'ner_deid_enriched']:::['jsl_rd_ner_wip_greedy_clinical', 'ner_jsl_enriched', 'ner_covid_trials', 'ner_jsl', 'jsl_ner_wip_modifier_clinical', 'ner_jsl_greedy']:::['ner_medmentions_coarse', 'jsl_rd_ner_wip_greedy_clinical', 'ner_jsl_enriched', 'ner_jsl', 'jsl_ner_wip_modifier_clinical', 'ner_jsl_greedy']:::['ner_chemd_clinical']|medication:::drug:::treatment:::therapeutic procedure:::drug ingredient:::drug chemical:::diagnostic aid:::substance:::medical device:::diagnostic procedure:::administration:::measurement:::drug strength:::physiological reaction:::patient:::vaccine:::psychological condition:::abbreviation|
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Model Information
| Model Name: | sbertresolve_ner_model_finder |
| Compatibility: | Healthcare NLP 3.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sbert_embeddings] |
| Output Labels: | [models] |
| Language: | en |
| Case sensitive: | false |