Machine learning prediction of hospitalization costs for coronary artery bypass grafting operations.

Journal: Surgery
Published Date: May 16, 2024

Abstract

BACKGROUND: With the steady rise in health care expenditures, the examination of factors that may influence the costs of care has garnered much attention. Although machine learning models have previously been applied in health economics, their application within cardiac surgery remains limited. We evaluated several machine learning algorithms to model hospitalization costs for coronary artery bypass grafting.

Authors

  • Emma O Cruz
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA; Computer Science Department, Stanford University, Palo Alto, CA.
  • Sara Sakowitz
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA.
  • Saad Mallick
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA.
  • Nguyen Le
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA.
  • Nikhil Chervu
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA.
  • Syed Shahyan Bakhtiyar
    Cardiovascular Outcomes Research Laboratories, David Geffen School of Medicine at University of California-Las Angeles, CA; Division of Cardiac Surgery, Department of Surgery, David Geffen School of Medicine at University of California-Las Angeles, CA; Department of Surgery, University of Colorado Anschutz Medical Center, Aurora, CO.
  • Peyman Benharash

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