Evaluation of Machine Learning Algorithms for Predicting Readmission After Acute Myocardial Infarction Using Routinely Collected Clinical Data.

Journal: The Canadian journal of cardiology
Published Date: Oct 25, 2019

Abstract

BACKGROUND: The ability to predict readmission accurately after hospitalization for acute myocardial infarction (AMI) is limited in current statistical models. Machine-learning (ML) methods have shown improved predictive ability in various clinical contexts, but their utility in predicting readmission after hospitalization for AMI is unknown.

Authors

  • Shagun Gupta
    Department of Mechanical and Industrial Engineering, University of Toronto, Ontario, Canada.
  • Dennis T Ko
    Schulich Heart Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada; Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada. Electronic address: dennis.ko@ices.on.ca.
  • Paymon Azizi
    Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
  • Mohamed Reda Bouadjenek
    Department of Mechanical and Industrial Engineering, University of Toronto, Ontario, Canada.
  • Maria Koh
    Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada.
  • Alice Chong
    Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada.
  • Peter C Austin
    Institute for Clinical Evaluative Service (ICES), Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
  • Scott Sanner
    Department of Mechanical and Industrial Engineering, University of Toronto, Ontario, Canada.

Keywords

External Resources

Popular Topics
Recent Journals