A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID-19 patients.

Journal: BMC medical informatics and decision making
Published Date: Jul 25, 2022

Abstract

BACKGROUND: Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-19 patients and updated the prediction in 6-h intervals during the first 72 h after hospital admission.

Authors

  • M Pishgar
    Department of Mechanical and Industrial Engineering, University of Illinois at Chicago (UIC), 842 W Taylor Street, MC 251, Chicago, IL, 60607, USA.
  • S Harford
    Department of Mechanical and Industrial Engineering, University of Illinois at Chicago (UIC), 842 W Taylor Street, MC 251, Chicago, IL, 60607, USA.
  • J Theis
    Department of Mechanical and Industrial Engineering, University of Illinois at Chicago (UIC), 842 W Taylor Street, MC 251, Chicago, IL, 60607, USA.
  • W Galanter
    Departments of Medicine and Pharmacy Systems, Outcomes and Policy, UIC, Chicago, USA.
  • J M Rodríguez-Fernández
    Department of Neurology, Clinical Informatics Fellowship, UIC, Chicago, USA.
  • L H Chaisson
    Department of Medicine, UIC, Chicago, USA.
  • Y Zhang
    University Technology Sydney, 15 Broadway, Ultimo, NSW Australia.
  • A Trotter
    Department of Medicine, UIC, Chicago, USA.
  • K M Kochendorfer
    Department of Family and Community Medicine, UIC, Chicago, USA.
  • A Boppana
    University of Illinois Hospital (UIH), UIC, Chicago, USA.
  • H Darabi
    Department of Mechanical and Industrial Engineering, University of Illinois at Chicago (UIC), 842 W Taylor Street, MC 251, Chicago, IL, 60607, USA. hdarabi@uic.edu.

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