Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis.

Journal: The Journal of thoracic and cardiovascular surgery
Published Date: Aug 10, 2020

Abstract

BACKGROUND: Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML model over traditional logistic regression (LR) remains controversial. We performed a systematic review and meta-analysis of studies comparing the discrimination accuracy between ML models versus LR in predicting operative mortality following cardiac surgery.

Authors

  • Umberto Benedetto
    Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom. Electronic address: umberto.benedetto@bristol.ac.uk.
  • Arnaldo Dimagli
    Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom.
  • Shubhra Sinha
    Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom.
  • Lucia Cocomello
    Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom.
  • Ben Gibbison
    NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK. ben.gibbison@bristol.ac.uk.
  • Massimo Caputo
    Department of Translational Health Sciences, Bristol Heart Institute, University of Bristol, London, United Kingdom.
  • Tom Gaunt
    Population Health Sciences, University of Bristol, London, United Kingdom.
  • Matt Lyon
    Population Health Sciences, University of Bristol, London, United Kingdom.
  • Chris Holmes
    Department of Statistics, University of Oxford, Oxford, UK.
  • Gianni D Angelini
    School of Clinical Sciences University of Bristol Bristol UK.

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