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

October 15, 2019 ยท Declared Dead ยท ๐Ÿ› International Conference on Machine Learning and Applications

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Authors Parminder Bhatia, Busra Celikkaya, Mohammed Khalilia, Selvan Senthivel arXiv ID 1910.07419 Category cs.CL: Computation & Language Citations 57 Venue International Conference on Machine Learning and Applications Last Checked 2 months ago
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.
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