Leveraging explainable artificial intelligence to optimize clinical decision support.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 38383050

Abstract

OBJECTIVE: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

Authors

  • Siru Liu
    School of Medicine, University of Utah, Salt Lake City, Utah, US.
  • Allison B McCoy
    Vanderbilt University Medical Center, Nashville, TN.
  • Josh F Peterson
    Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Thomas A Lasko
    Vanderbilt University School of Medicine, Nashville, TN.
  • Dean F Sittig
    Center for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center, Houston, Texas, USA.
  • Scott D Nelson
    George E. Whalen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA; University of Utah, Salt Lake City, UT, USA.
  • Jennifer Andrews
    Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Lorraine Patterson
    HeathIT, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Cheryl M Cobb
    Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • David Mulherin
    HeathIT, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Colleen T Morton
    Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
  • Adam Wright
    Harvard Medical School, Boston, MA; Brigham and Women's Hospital, Boston, MA.

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