Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach.

Journal: Computers in biology and medicine
PMID: 38626511

Abstract

BACKGROUND: Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explainability of machine learning algorithms in predicting unplanned readmission and death in cardiovascular patients at 30 days and 180 days from discharge.

Authors

  • Leo C E Huberts
    Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia. Electronic address: l.huberts@unsw.edu.au.
  • Sihan Li
    Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia.
  • Victoria Blake
    Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia; Eastern Heart Clinic, Prince of Wales Hospital, Sydney, NSW, Australia.
  • Louisa Jorm
    Centre for Big Data Research in Health, University of New South Wales, Sydney, NSW, Australia.
  • Jennifer Yu
    Washington University School of Medicine, Department of Surgery, Section of Abdominal Transplant and Hepatobiliary Surgery, Saint Louis, MO, USA.
  • Sze-Yuan Ooi
  • Blanca Gallego
    Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.

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