Forecasting the fall: the role of machine learning in predicting intraoperative hypotension, a scoping review.

Journal: Minerva anestesiologica
Published Date: Jul 30, 2025

Abstract

INTRODUCTION: Intraoperative hypotension is associated with increased risk of postoperative mortality, myocardial injury, acute kidney injury and stroke. Early identification with machine learning models allows pre-emptive management to reduce incidence and duration of intraoperative hypotension. This study aims to assess the accuracy of machine learning models in predicting intraoperative hypotension and its impact on clinical outcomes.

Authors

  • Angelina Koh
    The Royal Melbourne Hospital, Melbourne, Australia - angelina.koh@mh.org.au.
  • Dhanya Baby
    The Royal Melbourne Hospital, Melbourne, Australia.
  • Walston Martis
    Department of Critical Care, The University of Melbourne, Melbourne, Australia.
  • Daniel Capurro
    School of Computing and Information Systems, The University of Melbourne, Victoria, Australia; Centre for Digital Transformation of Health, Melbourne Medical School, The University of Melbourne, Victoria, Australia. Electronic address: dcapurro@unimelb.edu.au.

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