Development and validation of 'Patient Optimizer' (POP) algorithms for predicting surgical risk with machine learning.

Journal: BMC medical informatics and decision making
PMID: 38468330

Abstract

BACKGROUND: Pre-operative risk assessment can help clinicians prepare patients for surgery, reducing the risk of perioperative complications, length of hospital stay, readmission and mortality. Further, it can facilitate collaborative decision-making and operational planning.

Authors

  • Gideon Kowadlo
    Cerenaut, Melbourne, Australia.
  • Yoel Mittelberg
    Atidia Health, Melbourne, Australia.
  • Milad Ghomlaghi
    Atidia Health, Melbourne, Australia.
  • Daniel K Stiglitz
    Atidia Health, Melbourne, Australia.
  • Kartik Kishore
    Data Analytics Research and Evaluation (DARE) Centre, Austin Health and The University of Melbourne, Austin Hospital, Melbourne, Heidelberg, VIC, Australia.
  • Ranjan Guha
    Department of Anaesthesia, Austin Health, Heidelberg, Australia.
  • Justin Nazareth
    Department of Anaesthesia, Austin Health, Heidelberg, Australia.
  • Laurence Weinberg
    Department of Anesthesia, University of Melbourne, Victoria, Australia; Department of Surgery and Centre for Anesthesia, Perioperative and Pain Medicine, University of Melbourne, Victoria, Australia. Electronic address: laurence.weinberg@austin.org.au.

Keywords

External Resources

Popular Topics
Recent Journals