Spark NLP in Action

Spark NLP for Healthcare Recognize Clinical Entities
Detect clinical entities in text
Automatically detect more than 50 clinical entities using our NER deep learning model.
Detect signs and symptoms
Automatically identify Signs and Symptoms in clinical documents using two of our pretrained Spark NLP clinical models.
Detect diagnosis and procedures
Automatically identify diagnoses and procedures in clinical documents using the pretrained Spark NLP clinical model ner_clinical.
Detect drugs and prescriptions
Automatically identify Drug, Dosage, Duration, Form, Frequency, Route, and Strength details in clinical documents using three of our pretrained Spark NLP clinical models.
Identify diagnosis and symptoms assertion status
Automatically detect if a diagnosis or a symptom is present, absent, uncertain or associated to other persons (e.g. family members).
Adverse drug events tagger
Automatic pipeline that tags documents as containing or not containing adverse events description, then identifies those events.
Detect anatomical references
Automatically identify Anatomical System, Cell, Cellular Component, Anatomical Structure, Immaterial Anatomical Entity, Multi-tissue Structure, Organ, Organism Subdivision, Organism Substance, Pathological Formation in clinical documents using our pretrained Spark NLP model.
Detect clinical events
Automatically identify a variety of clinical events such as Problems, Tests, Treatments, Admissions or Discharges, in clinical documents using two of our pretrained Spark NLP models.
Detect lab results
Automatically identify Lab test names and Lab results from clinical documents using our pretrained Spark NLP model.
Detect risk factors
Automatically identify risk factors such as Coronary artery disease, Diabetes, Family history, Hyperlipidemia, Hypertension, Medications, Obesity, PHI, Smoking habits in clinical documents using our pretrained Spark NLP model.
Detect Drug Chemicals (Bert For Token Classification)
This demo shows how drug chemicals can be extracted from medical texts using Spark NLP model which trained with BertForTokenClassifier.
Detect Wide Range of Clinical Entities (Bert For Token Classification)
This demo shows how clinical terminology can be extracted from medical texts using Spark NLP model which trained with BertForTokenClassifier.
Detect Wide Range of More Generalized Clinical Entities (Bert For Token Classification)
This demo shows how clinical terminology can be extracted from medical texts using Spark NLP model which trained with BertForTokenClassifier.
Detect Covid-related clinical terminology
This demo shows how Covid-related clinical terminology can be detected using a Spark NLP Healthcare NER model.
Find available models for your clinical entities
This demo shows how to use a pretrained pipeline to find the best NER model given an entity name.
Extract Drugs and Chemicals
This demo shows how Names of Drugs & Chemicals can be detected using a Spark NLP Healthcare NER model.