Named Entity Recognition Profiling (Biobert)

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 biobert_pubmed_base_cased. It has been updated by adding NER model outputs to the previous version.

Here are the NER models that this pretrained pipeline includes: ner_jsl_enriched_biobert, ner_clinical_biobert, ner_chemprot_biobert, ner_jsl_greedy_biobert, ner_bionlp_biobert, ner_human_phenotype_go_biobert, jsl_rd_ner_wip_greedy_biobert, ner_posology_large_biobert, ner_risk_factors_biobert, ner_anatomy_coarse_biobert, ner_deid_enriched_biobert, ner_human_phenotype_gene_biobert, ner_jsl_biobert, ner_events_biobert, ner_deid_biobert, ner_posology_biobert, ner_diseases_biobert, jsl_ner_wip_greedy_biobert, ner_ade_biobert, ner_anatomy_biobert, ner_cellular_biobert .

Predicted Entities

ADE, ADMISSION, AGE, Admission_Discharge, Age, Alcohol, Allergen, Allergenic_substance, Amino_acid, Anatomical_system, Anatomy, BIOID, BMI, Birth_Entity, Blood_Pressure, BodyPart, CAD, CHEMICAL, CITY, CLINICAL_DEPT, CONTACT, COUNTRY, Cancer, Cancer_Modifier, Causative_Agents_(Virus_and_Bacteria), Cell, Cellular_component, Cerebrovascular_Disease, Clinical_Dept, Communicable_Disease, DATE, DEVICE, DIABETES, DISCHARGE, DNA, DOCTOR, DOSAGE, DRUG, DURATION, Date, Death_Entity, Developing_anatomical_structure, Diabetes, Diagnosis, Diet, Direction, Disease, Disease_Syndrome_Disorder, Dosage, Drug, Drug_BrandName, Drug_Ingredient, Drug_Name, Duration, EKG_Findings, EMAIL, EVIDENTIAL, Employment, External_body_part_or_region, FAMILY_HIST, FAX, FORM, FREQUENCY, Family_History_Header, Female_Reproductive_Status, Fetus_NewBorn, Form, Frequency, GENE, GENE-N, GENE-Y, GO, Gender, Gene_or_gene_product, HDL, HEALTHPLAN, HOSPITAL, HP, HUMAN, HYPERLIPIDEMIA, HYPERTENSION, Heart_Disease, Height, Hyperlipidemia, Hypertension, ID, IDNUM, ImagingFindings, ImagingTest, Imaging_Technique, Immaterial_anatomical_entity, Injury_or_Poisoning, Internal_organ_or_component, Kidney_Disease, LDL, LOCATION, LOCATION-OTHER, Lab_Name, Lab_Result, Labour_Delivery, MEDICALRECORD, MEDICATION, ManualFix, Maybe, Measurements, Medical_Device, Medical_History_Header, Metastasis, Modifier, Multi-tissue_structure, NAME, Name, Negation, O2_Saturation, OBESE, OCCURRENCE, ORGANIZATION, Obesity, Oncological, Oncology_Therapy, Organ, Organism, Organism_subdivision, Organism_substance, OtherFindings, Overweight, Oxygen_Therapy, PATIENT, PHI, PHONE, PROBLEM, PROFESSION, Pathological_formation, Performance_Status, Pregnancy, Pregnancy_Delivery_Puerperium, Procedure, Procedure_Name, Psychological_Condition, Puerperium, Pulse, Pulse_Rate, RNA, ROUTE, Race_Ethnicity, Relationship_Status, RelativeDate, RelativeTime, Respiration, Respiratory_Rate, Route, SMOKER, SPECIES, STATE, STREET, STRENGTH, Score, Section_Header, Section_Name, Sexually_Active_or_Sexual_Orientation, Simple_chemical, Smoking, Social_History_Header, Staging, Strength, Substance, Substance_Name, Substance_Quantity, Symptom, Symptom_Name, TEST, TIME, TREATMENT, Temperature, Test, Test_Result, Time, Tissue, Total_Cholesterol, Treatment, Triglycerides, Tumor_Finding, URL, USERNAME, Units, VS_Finding, Vaccine, Vital_Signs_Header, Weight, ZIP, cell_line, cell_type, protein

Live Demo Open in Colab Download Copy S3 URI

How to use

from sparknlp.pretrained import PretrainedPipeline

ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', '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_biobert', '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_biobert").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_diseases_biobert Model Results ******************** 

[('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]


******************** ner_events_biobert Model Results ******************** 

[('gestational diabetes mellitus', 'PROBLEM'), ('eight years', 'DURATION'), ('presentation', 'OCCURRENCE'), ('type two diabetes mellitus ( T2DM', 'PROBLEM'), ('HTG-induced pancreatitis', 'PROBLEM'), ('three years', 'DURATION'), ('presentation', 'OCCURRENCE'), ('an acute hepatitis', 'PROBLEM'), ('obesity', 'PROBLEM'), ('a body mass index', 'TEST'), ('BMI', 'TEST'), ('presented', 'OCCURRENCE'), ('a one-week', 'DURATION'), ('polyuria', 'PROBLEM'), ('polydipsia', 'PROBLEM'), ('poor appetite', 'PROBLEM'), ('vomiting', 'PROBLEM')]


******************** ner_jsl_biobert Model Results ******************** 

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


******************** ner_clinical_biobert Model Results ******************** 

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


******************** ner_risk_factors_biobert Model Results ******************** 

[('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]

...

Model Information

Included Models

• DocumentAssembler
• SentenceDetectorDLModel
• TokenizerModel
• BertEmbeddings
• 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