Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 32417928

Abstract

OBJECTIVE: To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions.

Authors

  • Seyedeh Neelufar Payrovnaziri
    School of Information, Florida State University, Tallahassee, Florida, USA.
  • Zhaoyi Chen
    Department of Epidemiology, College of Medicine & College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
  • Pablo Rengifo-Moreno
    College of Medicine, Florida State University, Tallahassee, Florida, USA.
  • Tim Miller
    School of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia.
  • Jiang Bian
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America.
  • Jonathan H Chen
    Stanford Center for Biomedical Informatics Research, Stanford, CA.
  • Xiuwen Liu
    Department of Computer Science, Florida State University, Tallahassee, FL 32306-4530, United States. Electronic address: liux@cs.fsu.edu.
  • Zhe He
    School of Information, Florida State University, Tallahassee, FL, USA.

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