Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.

Journal: JAMA network open
Published Date: Dec 7, 2018

Abstract

IMPORTANCE: Accurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, using only the single worst value of laboratory tests and vital signs and largely ignoring information present in free-text notes. Whether capturing more of the available data and applying machine learning and natural language processing (NLP) can improve and automate the prediction of outcomes among patients in the ICU remains unknown.

Authors

  • Ben J Marafino
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • Miran Park
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • Jason M Davies
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • Robert Thombley
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • Harold S Luft
    Palo Alto Medical Foundation Research Institute, Palo Alto, California.
  • David C Sing
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • Dhruv S Kazi
    Division of Cardiology, Zuckerberg San Francisco General Hospital, San Francisco, California.
  • Colette DeJong
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • W John Boscardin
    Department of Epidemiology and Biostatistics, University of California, San Francisco.
  • Mitzi L Dean
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • R Adams Dudley
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.

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