Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients.

Journal: JAMA network open
Published Date: Oct 5, 2018

Abstract

IMPORTANCE: To improve patient safety, health care systems need reliable methods to detect adverse events in large patient populations. Events are often described in clinical notes, rather than structured data, which make them difficult to identify on a large scale.

Authors

  • Maxwell Taggart
    Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City.
  • Wendy W Chapman
    School of Medicine, University of Utah, Salt Lake City, Utah, US.
  • Benjamin A Steinberg
    School of Medicine, University of Utah, SLC, UT, USA.
  • Shane Ruckel
    Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.
  • Arianna Pregenzer-Wenzler
    Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.
  • Yishuai Du
    Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City.
  • Jeffrey Ferraro
    Intermountain Healthcare, Salt Lake City, UT, USA.
  • Brian T Bucher
    School of Medicine, University of Utah, Salt Lake City, Utah, US.
  • Donald M Lloyd-Jones
    Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
  • Matthew T Rondina
    Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.
  • Rashmee U Shah
    Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City.

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