Predictive Utility of a Machine Learning Algorithm in Estimating Mortality Risk in Cardiac Surgery.

Journal: The Annals of thoracic surgery
PMID: 31706872

Abstract

BACKGROUND: This study evaluated the predictive utility of a machine learning algorithm in estimating operative mortality risk in cardiac surgery.

Authors

  • Arman Kilic
    Division of Cardiac Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania. Electronic address: kilica2@upmc.edu.
  • Anshul Goyal
    Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  • James K Miller
    Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  • Eva Gjekmarkaj
    Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  • Weng Lam Tam
    Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  • Thomas G Gleason
    Division of Cardiac Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Ibrahim Sultan
    Division of Cardiac Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
  • Artur Dubrawksi
    Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania.

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