Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure.

Journal: International journal of cardiology
Published Date: Jul 20, 2022

Abstract

BACKGROUND: Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints.

Authors

  • David E Austin
    ICES, Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, Toronto, ON, M4N3M5, Canada.
  • Douglas S Lee
    Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada; Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Institute for Health Policy, Management and Evaluation, Toronto, Ontario, Canada; Toronto General Hospital Research Institute, Toronto, Ontario, Canada; University of Toronto, Toronto, Ontario, Canada. Electronic address: dlee@ices.on.ca.
  • Chloe X Wang
    University Health Network, Toronto, Canada.
  • Shihao Ma
    Department of Computer Science, University of Toronto, Toronto, ON, Canada.
  • XueSong Wang
  • Joan Porter
    ICES, Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, Toronto, ON, M4N3M5, Canada.
  • Bo Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.

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