Use of machine learning in pediatric surgical clinical prediction tools: A systematic review.

Journal: Journal of pediatric surgery
Published Date: Jan 19, 2023

Abstract

PURPOSE: Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artificial intelligence have resulted in a proliferation of CPTs created using machine learning (ML)-yet the clinical applicability of ML-based CPTs and their validation in clinical settings remain unclear. This systematic review aims to compare the validity and clinical efficacy of ML-based to traditional CPTs in pediatric surgery.

Authors

  • Amanda Bianco
    Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.
  • Zaid A M Al-Azzawi
    Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.
  • Elena Guadagno
    Harvey E. Beardmore Division of Pediatric Surgery, Montreal Children's Hospital, McGill University Health Center and McGill University, Montreal, Canada. Electronic address: elena.guadagno@muhc.mcgill.ca.
  • Esli Osmanlliu
    Department of Pediatrics, McGill University Health Centre, Montreal, Quebec, Canada.
  • Jocelyn Gravel
    Department of Pediatric Emergency Medicine, Sainte-Justine Hospital, Université de Montréal, Montreal, Quebec, Canada.
  • Dan Poenaru
    Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada; Department of Pediatric Surgery, McGill University, Canada.

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