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      Automated Tools for Phenotype Extraction from Medical Records

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          Abstract

          Clinical research studying critical illness phenotypes relies on the identification of clinical syndromes defined by consensus definitions. Historically, identifying phenotypes has required manual chart review, a time and resource intensive process. The overall research goal of C ritical I llness PH enotype E xt R action (deCIPHER) project is to develop automated approaches based on natural language processing and machine learning that accurately identify phenotypes from EMR. We chose pneumonia as our first critical illness phenotype and conducted preliminary experiments to explore the problem space. In this abstract, we outline the tools we built for processing clinical records, present our preliminary findings for pneumonia extraction, and describe future steps.

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          Author and article information

          Journal
          AMIA Summits Transl Sci Proc
          AMIA Summits Transl Sci Proc
          AMIA Summits on Translational Science Proceedings
          American Medical Informatics Association
          2153-4063
          2013
          18 March 2013
          : 2013
          : 283
          Affiliations
          [ 1 ] Biomedical and Health Informatics
          [ 2 ] Department of Linguistics
          [ 3 ] Department of Surgery
          [ 4 ] Pulmonary and Critical Care Medicine, University of Washington, Seattle, WA;
          [ 5 ] Microsoft Research, Redmond, WA
          Article
          amia_cri_2013_283
          3845784
          24303281
          3d94861c-cd0a-4cc3-8692-ed2a0605b43e
          ©2013 AMIA - All rights reserved.
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          Medicine
          Medicine

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