Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service

TL;DR

Comprehend Medical is a scalable, HIPAA-compliant web service that performs named entity recognition and relationship extraction in medical texts using deep learning, with easy API access and contextual analysis.

Contribution

It introduces a cloud-based, easy-to-use medical NER and RE service that does not require complex setup or dependencies, unlike many open-source tools.

Findings

Supports five medical categories for NER: Anatomy, Condition, Medications, PHI, TTP.

Provides relationship extraction and contextual traits like negation and temporality.

Accessible via AWS Console, Java, and Python SDKs.

Abstract

Comprehend Medical is a stateless and Health Insurance Portability and Accountability Act (HIPAA) eligible Named Entity Recognition (NER) and Relationship Extraction (RE) service launched under Amazon Web Services (AWS) trained using state-of-the-art deep learning models. Contrary to many existing open source tools, Comprehend Medical is scalable and does not require steep learning curve, dependencies, pipeline configurations, or installations. Currently, Comprehend Medical performs NER in five medical categories: Anatomy, Medical Condition, Medications, Protected Health Information (PHI) and Treatment, Test and Procedure (TTP). Additionally, the service provides relationship extraction for the detected entities as well as contextual information such as negation and temporality in the form of traits. Comprehend Medical provides two Application Programming Interfaces (API): 1) the NERe API which returns all the extracted named entities, their traits and the relationships between them and 2) the PHId API which returns just the protected health information contained in the text. Furthermore, Comprehend Medical is accessible through AWS Console, Java and Python Software Development Kit (SDK), making it easier for non-developers and developers to use.