Identifying infected patients using semi-supervised and transfer learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Sep 12, 2022

Abstract

OBJECTIVES: Early identification of infection improves outcomes, but developing models for early identification requires determining infection status with manual chart review, limiting sample size. Therefore, we aimed to compare semi-supervised and transfer learning algorithms with algorithms based solely on manual chart review for identifying infection in hospitalized patients.

Authors

  • Fereshteh S Bashiri
    Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA.
  • John R Caskey
    Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA.
  • Anoop Mayampurath
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
  • Nicole Dussault
    Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA.
  • Jay Dumanian
    Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA.
  • Sivasubramanium V Bhavani
    Department of Medicine, University of Chicago Medical Center, Chicago, IL.
  • Kyle A Carey
    Department of Medicine, University of Chicago, Chicago IL, United States.
  • Emily R Gilbert
    Department of Medicine, Loyola University Medical Center, Maywood, Illinois.
  • Christopher J Winslow
    Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois, USA.
  • Nirav S Shah
    Department of Medicine, Northshore Hospital, Chicago, IL.
  • Dana P Edelson
  • Majid Afshar
    Loyola University Chicago, Chicago, IL.
  • Matthew M Churpek
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.

Keywords

External Resources

Popular Topics
Recent Journals