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.

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

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_cellular_biobert_chunks :  []
ner_diseases_biobert_chunks :  ['gestational diabetes mellitus', 'type two diabetes mellitus', 'T2DM', 'HTG-induced pancreatitis', 'hepatitis', 'obesity', 'polyuria', 'polydipsia', 'poor appetite', 'vomiting']
ner_events_biobert_chunks :  ['gestational diabetes mellitus', 'eight years', 'presentation', 'type two diabetes mellitus ( T2DM', 'HTG-induced pancreatitis', 'three years', 'presentation', 'an acute hepatitis', 'obesity', 'a body mass index', 'BMI', 'presented', 'a one-week', 'polyuria', 'polydipsia', 'poor appetite', 'vomiting']
ner_bionlp_biobert_chunks :  []
ner_jsl_greedy_biobert_chunks :  ['28-year-old', 'female', 'gestational diabetes mellitus', 'eight years prior', 'type two diabetes mellitus', 'T2DM', 'HTG-induced pancreatitis', 'three years prior', 'acute hepatitis', 'obesity', 'body mass index', 'BMI ) of 33.5 kg/m2', 'one-week', 'polyuria', 'polydipsia', 'poor appetite', 'vomiting']
ner_jsl_biobert_chunks :  ['28-year-old', 'female', 'gestational diabetes mellitus', 'eight years prior', 'type two diabetes mellitus', 'T2DM', 'HTG-induced pancreatitis', 'three years prior', 'acute', 'hepatitis', 'obesity', 'body mass index', 'BMI ) of 33.5 kg/m2', 'one-week', 'polyuria', 'polydipsia', 'poor appetite', 'vomiting']
ner_anatomy_biobert_chunks :  ['body']
ner_jsl_enriched_biobert_chunks :  ['28-year-old', 'female', 'gestational diabetes mellitus', 'type two diabetes mellitus', 'T2DM', 'HTG-induced pancreatitis', 'acute', 'hepatitis', 'obesity', 'polyuria', 'polydipsia', 'poor appetite', 'vomiting']
ner_human_phenotype_go_biobert_chunks :  ['obesity', 'polyuria', 'polydipsia']
ner_deid_biobert_chunks :  ['eight years', 'three years']
ner_deid_enriched_biobert_chunks :  []
token :  ['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', '.']
ner_clinical_biobert_chunks :  ['gestational diabetes mellitus', 'subsequent type two diabetes mellitus ( T2DM', 'HTG-induced pancreatitis', 'an acute hepatitis', 'obesity', 'a body mass index ( BMI )', 'polyuria', 'polydipsia', 'poor appetite', 'vomiting']
ner_anatomy_coarse_biobert_chunks :  ['body']
ner_human_phenotype_gene_biobert_chunks :  ['obesity', 'mass', 'polyuria', 'polydipsia', 'vomiting']
ner_posology_large_biobert_chunks :  []
jsl_rd_ner_wip_greedy_biobert_chunks :  ['gestational diabetes mellitus', 'type two diabetes mellitus', 'T2DM', 'HTG-induced pancreatitis', 'acute hepatitis', 'obesity', 'body mass index', '33.5', 'kg/m2', 'polyuria', 'polydipsia', 'poor appetite', 'vomiting']
ner_posology_biobert_chunks :  []
jsl_ner_wip_greedy_biobert_chunks :  ['28-year-old', 'female', 'gestational diabetes mellitus', 'eight years prior', 'type two diabetes mellitus', 'T2DM', 'HTG-induced pancreatitis', 'three years prior', 'acute hepatitis', 'obesity', 'body mass index', 'BMI ) of 33.5 kg/m2', 'one-week', 'polyuria', 'polydipsia', 'poor appetite', 'vomiting']
ner_chemprot_biobert_chunks :  []
ner_ade_biobert_chunks :  ['pancreatitis', 'acute hepatitis', 'polyuria', 'polydipsia', 'poor appetite', 'vomiting']
ner_risk_factors_biobert_chunks :  ['diabetes mellitus', 'subsequent type two diabetes mellitus', 'obesity']
sentence :  ['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 .']

Model Information

Model Name:	ner_profiling_biobert
Type:	pipeline
Compatibility:	Healthcare NLP 3.2.3+
License:	Licensed
Edition:	Official
Language:	en

Included Models

DocumentAssembler
SentenceDetectorDLModel
TokenizerModel
BertEmbeddings
MedicalNerModel (x21)
NerConverter (x21)
Finisher

PREVIOUSDetect Drug Chemicals (BertForTokenClassifier)

NEXTNamed Entity Recognition Profiling (Clinical)