Personalizing patient risk of a life-altering event: An application of machine learning to hemiarch surgery.

Journal: The Journal of thoracic and cardiovascular surgery
PMID: 38685466

Abstract

OBJECTIVE: The study objective was to assess a machine learning model's ability to predict the occurrence of life-altering events in hemiarch surgery and determine contributing patient characteristics and intraoperative factors.

Authors

  • Adam M Carroll
    Division of Cardiothoracic Surgery, Department of Surgery, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colo. Electronic address: adam.carroll@cuanschutz.edu.
  • Nicolas Chanes
    Division of Cardiothoracic Surgery, Department of Surgery, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colo.
  • Ananya Shah
    Division of Cardiothoracic Surgery, Department of Surgery, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colo.
  • Lance Dzubinski
    Division of Cardiothoracic Surgery, Department of Surgery, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colo.
  • Muhammad Aftab
    Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
  • T Brett Reece
    Division of Cardiothoracic Surgery, Department of Surgery, Anschutz Medical Campus, University of Colorado School of Medicine, Aurora, Colo.

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