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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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      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
      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
      24303281
      3845784
      amia_cri_2013_283
      ©2013 AMIA - All rights reserved.
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      Medicine

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