Development and external validation of deep learning clinical prediction models using variable-length time series data.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 38679906

Abstract

OBJECTIVES: To compare and externally validate popular deep learning model architectures and data transformation methods for variable-length time series data in 3 clinical tasks (clinical deterioration, severe acute kidney injury [AKI], and suspected infection).

Authors

  • Fereshteh S Bashiri
    Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA.
  • Kyle A Carey
    Department of Medicine, University of Chicago, Chicago IL, United States.
  • Jennie Martin
    Department of Medicine, University of Wisconsin-Madison, Madison, WI 53792, United States.
  • Jay L Koyner
    Section of Nephrology, Department of Medicine, University of Chicago, Chicago, Illinois.
  • Dana P Edelson
  • Emily R Gilbert
    Department of Medicine, Loyola University Medical Center, Maywood, Illinois.
  • Anoop Mayampurath
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
  • Majid Afshar
    Loyola University Chicago, Chicago, IL.
  • Matthew M Churpek
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.

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