A comparative study of machine learning models predicting post-hepatectomy liver failure: enhancing risk estimation in over 25,000 National Surgical Quality Improvement Program patients.

Journal: Annals of hepato-biliary-pancreatic surgery
Published Date: Jul 7, 2025

Abstract

BACKGROUNDS/AIMS: Post-hepatectomy liver failure (PHLF) is a significant complication with an incidence rate between 8% and 12%. Machine learning (ML) can analyze large datasets to uncover patterns not apparent through traditional methods, enhancing PHLF prediction and potentially mitigate complications.

Authors

  • Gautham Nair
    Schulich School of Medicine, University of Western Ontario, London, ON, Canada.
  • Ali Hadi
    Schulich School of Medicine, University of Western Ontario, London, ON, Canada.
  • Kartik Gupta
    Schulich School of Medicine, University of Western Ontario, London, ON, Canada.
  • Edward Tran
    Department of Computer Science, Stanford University, Stanford, CA, United States.
  • Geerthan Srikantharajah
    Toronto Metropolitan University, Toronto, ON, Canada.
  • Evelyn Waugh
    Schulich School of Medicine, University of Western Ontario, London, ON, Canada.
  • Ephraim Tang
    Schulich School of Medicine, University of Western Ontario, London, ON, Canada.
  • Anton Skaro
    Schulich School of Medicine, University of Western Ontario, London, ON, Canada.
  • Juan Glinka
    Schulich School of Medicine, University of Western Ontario, London, ON, Canada.

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