Spark NLP in Action

Automatically identify Signs and Symptoms in clinical documents using two of our pretrained Spark NLP clinical models.

Automatically identify diagnoses and procedures in clinical documents using the pretrained Spark NLP clinical model ner_clinical.

Automatically identify Drug, Dosage, Duration, Form, Frequency, Route, and Strength details in clinical documents using three of our pretrained Spark NLP clinical models.

Automatic pipeline that tags documents as containing or not containing adverse events description, then identifies those events.

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.

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.

Automatically identify Lab test names and Lab results from clinical documents using our pretrained Spark NLP model.

Automatically identify tumor characteristics such as Anatomical systems, Cancer, Cells, Cellular components, Genes and gene products, Multi-tissue structures, Organs, Organisms, Organism subdivisions, Simple chemicals, Tissues from clinical documents using our pretrained Spark NLP model.

Automatically detect more than 50 clinical entities using our NER deep learning model.

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.

Automatically detect if a diagnosis or a symptom is present, absent, uncertain or associated to other persons (e.g. family members).