Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta-analysis.

Journal: Journal of cardiac surgery
Published Date: Aug 24, 2022

Abstract

BACKGROUND: Machine learning (ML) models are promising tools for predicting adverse postoperative outcomes in cardiac surgery, yet have not translated to routine clinical use. We conducted a systematic review and meta-analysis to assess the predictive performance of ML approaches.

Authors

  • Jahan C Penny-Dimri
    Department of Surgery, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
  • Christoph Bergmeir
    Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia.
  • Luke Perry
    Department of Anaesthesia and Pain Management, Royal Melbourne Hospital, Melbourne, Victoria, Australia.
  • Linley Hayes
    Department of Anaesthesia, Barwon Health, Geelong, Victoria, Australia.
  • Rinaldo Bellomo
    Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, VIC, Australia.
  • Julian A Smith
    Department of Cardiothoracic Surgery, Monash Medical Centre, Melbourne, Australia.

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