Interpretable machine learning-based predictive modeling of patient outcomes following cardiac surgery.

Journal: The Journal of thoracic and cardiovascular surgery
PMID: 38040328

Abstract

BACKGROUND: The clinical applicability of machine learning predictions of patient outcomes following cardiac surgery remains unclear. We applied machine learning to predict patient outcomes associated with high morbidity and mortality after cardiac surgery and identified the importance of variables to the derived model's performance.

Authors

  • Adeel Abbasi
    Department of Medicine, Division of Pulmonary Critical Care and Sleep, Warren Alert Medical School of Brown University, Providence, Rhode Island, USA.
  • Cindy Li
    Brown University, Providence, RI, USA.
  • Max Dekle
    Brown University, Providence, RI.
  • Christian A Bermudez
    Division of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pa.
  • Daniel Brodie
    Division of Pulmonary and Critical Care, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Md.
  • Frank W Sellke
    Division of Cardiothoracic Surgery, Department of Surgery, Warren Alpert School of Medicine at Brown University, Providence, RI.
  • Neel R Sodha
    Department of Surgery, Warren Alpert Medical School of Brown University, Providence, RI, USA.
  • Corey E Ventetuolo
    Department of Medicine, Warren Alpert Medical School of Brown University, Providence, RI, USA. corey_ventetuolo@brown.edu.
  • Carsten Eickhoff
    Department of Computer Science, ETH Zurich, Zurich, Switzerland; Center for Biomedical Informatics, Brown University, Providence, RI, USA.

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