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  • John Snow Labs
  • Apr 28, 2023

Named Entity Recognition Profiling (Clinical)

  • licensed
  • en
  • clinical
  • profiling
  • ner_profiling
  • ner

Description

This pipeline can be used to explore all the available pretrained NER models at once. When you run this pipeline over your text, you will end up with the predictions coming out of each pretrained clinical NER model trained with embeddings_clinical. It has been updated by adding new clinical NER models and NER model outputs to the previous version.

Here are the NER models that this pretrained pipeline includes:

jsl_ner_wip_clinical,jsl_ner_wip_greedy_clinical,jsl_ner_wip_modifier_clinical, jsl_rd_ner_wip_greedy_clinical, ner_abbreviation_clinical, ner_ade_binary, ner_ade_clinical, ner_anatomy, ner_anatomy_coarse, ner_bacterial_species, ner_biomarker, ner_biomedical_bc2gm, ner_bionlp, ner_cancer_genetics, ner_cellular, ner_chemd_clinical, ner_chemicals, ner_chemprot_clinical, ner_chexpert, ner_clinical, ner_clinical_large, ner_clinical_trials_abstracts, ner_covid_trials, ner_deid_augmented, ner_deid_enriched, ner_deid_generic_augmented,ner_deid_large, ner_deid_sd,ner_deid_sd_large,ner_deid_subentity_augmented,ner_deid_subentity_augmented_i2b2, ner_deid_synthetic, ner_diseases, ner_diseases_large, ner_drugprot_clinical, ner_drugs, ner_drugs_greedy, ner_drugs_large, ner_eu_clinical_case, ner_eu_clinical_condition, ner_events_admission_clinical, ner_events_clinical, ner_financial_contract, ner_genetic_variants, ner_healthcare, ner_human_phenotype_gene_clinical, ner_human_phenotype_go_clinical, ner_jsl, ner_jsl_enriched, ner_jsl_slim, ner_living_species, ner_measurements_clinical, ner_medmentions_coarse, ner_nature_nero_clinical, ner_nihss, ner_oncology, ner_oncology_anatomy_general, ner_oncology_anatomy_granular, ner_oncology_biomarker, ner_oncology_demographics, ner_oncology_diagnosis, ner_oncology_posology, ner_oncology_response_to_treatment, ner_oncology_test, ner_oncology_therapy, ner_oncology_tnm, ner_oncology_unspecific_posology, ner_oncology_wip, ner_pathogen, ner_posology, ner_posology_experimental, ner_posology_greedy, ner_posology_large, ner_posology_small, ner_radiology, ner_radiology_wip_clinical, ner_risk_factors, ner_sdoh_access_to_healthcare_wip, ner_sdoh_community_condition_wip, ner_sdoh_demographics_wip, ner_sdoh_health_behaviours_problems_wip, ner_sdoh_income_social_status_wip, ner_sdoh_mentions, ner_sdoh_slim_wip, ner_sdoh_social_environment_wip, ner_sdoh_substance_usage_wip, ner_sdoh_wip, ner_supplement_clinical, ner_vop_anatomy_wip, ner_vop_clinical_dept_wip, ner_vop_demographic_wip, ner_vop_problem_reduced_wip, ner_vop_problem_wip, ner_vop_slim_wip, ner_vop_temporal_wip, ner_vop_test_wip, ner_vop_treatment_wip, ner_vop_wip, nerdl_tumour_demo

Download Copy S3 URI

How to use


from sparknlp.pretrained import PretrainedPipeline

ner_profiling_pipeline = PretrainedPipeline("ner_profiling_clinical", "en", "clinical/models")

result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")



import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_clinical", "en", "clinical/models")

val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")



import nlu

nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")

Results

******************** ner_jsl Model Results ******************** 

[('28-year-old', 'Age'), ('female', 'Gender'), ('gestational diabetes mellitus', 'Diabetes'), ('eight years prior', 'RelativeDate'), ('subsequent', 'Modifier'), ('type two diabetes mellitus', 'Diabetes'), ('T2DM', 'Diabetes'), ('HTG-induced pancreatitis', 'Disease_Syndrome_Disorder'), ('three years prior', 'RelativeDate'), ('acute', 'Modifier'), ('hepatitis', 'Communicable_Disease'), ('obesity', 'Obesity'), ('body mass index', 'Symptom'), ('33.5 kg/m2', 'Weight'), ('one-week', 'Duration'), ('polyuria', 'Symptom'), ('polydipsia', 'Symptom'), ('poor appetite', 'Symptom'), ('vomiting', 'Symptom')]


******************** ner_diseases_large Model Results ******************** 

[('gestational diabetes mellitus', 'Disease'), ('diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('vomiting', 'Disease')]


******************** ner_radiology Model Results ******************** 

[('gestational diabetes mellitus', 'Disease_Syndrome_Disorder'), ('type two diabetes mellitus', 'Disease_Syndrome_Disorder'), ('T2DM', 'Disease_Syndrome_Disorder'), ('HTG-induced pancreatitis', 'Disease_Syndrome_Disorder'), ('acute hepatitis', 'Disease_Syndrome_Disorder'), ('obesity', 'Disease_Syndrome_Disorder'), ('body', 'BodyPart'), ('mass index', 'Symptom'), ('BMI', 'Test'), ('33.5', 'Measurements'), ('kg/m2', 'Units'), ('polyuria', 'Symptom'), ('polydipsia', 'Symptom'), ('poor appetite', 'Symptom'), ('vomiting', 'Symptom')]


******************** ner_clinical Model Results ******************** 

[('gestational diabetes mellitus', 'PROBLEM'), ('subsequent type two diabetes mellitus', 'PROBLEM'), ('T2DM', 'PROBLEM'), ('HTG-induced pancreatitis', 'PROBLEM'), ('an acute hepatitis', 'PROBLEM'), ('obesity', 'PROBLEM'), ('a body mass index', 'PROBLEM'), ('BMI', 'TEST'), ('polyuria', 'PROBLEM'), ('polydipsia', 'PROBLEM'), ('poor appetite', 'PROBLEM'), ('vomiting', 'PROBLEM')]


******************** ner_medmentions_coarse Model Results ******************** 

[('female', 'Organism_Attribute'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('T2DM', 'Disease_or_Syndrome'), ('HTG-induced pancreatitis', 'Disease_or_Syndrome'), ('associated with', 'Qualitative_Concept'), ('acute hepatitis', 'Disease_or_Syndrome'), ('obesity', 'Disease_or_Syndrome'), ('body mass index', 'Clinical_Attribute'), ('BMI', 'Clinical_Attribute'), ('polyuria', 'Sign_or_Symptom'), ('polydipsia', 'Sign_or_Symptom'), ('poor appetite', 'Sign_or_Symptom'), ('vomiting', 'Sign_or_Symptom')]

...

Model Information

Model Name: ner_profiling_clinical
Type: pipeline
Compatibility: Healthcare NLP 4.4.0+
License: Licensed
Edition: Official
Language: en
Size: 3.1 GB

Included Models

  • DocumentAssembler
  • SentenceDetectorDLModel
  • TokenizerModel
  • WordEmbeddingsModel
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • MedicalNerModel
  • NerConverter
  • Finisher