Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians.

Journal: Current oncology (Toronto, Ont.)
Published Date: May 11, 2024

Abstract

This narrative review explores the utilization of machine learning (ML) and artificial intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer significant potential to improve perioperative cancer care by predicting outcomes and supporting clinical decision-making. Tailored for perioperative professionals including anesthesiologists, surgeons, critical care physicians, nurse anesthetists, and perioperative nurses, this review provides a comprehensive framework for the integration of ML and AI models to enhance patient care delivery throughout the perioperative continuum.

Authors

  • Garry Brydges
    Division of Anesthesiology, Critical Care & Pain Medicine, The University of Texas at MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Abhineet Uppal
    Department of Colon & Rectal Surgery, The University of Texas at MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Vijaya Gottumukkala
    Department of Anesthesiology & Perioperative Medicine, The University of Texas at MD Anderson Cancer Center, 1400-Unit 409, Holcombe Blvd, Houston, TX 77030, USA.

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