Five steps in performing machine learning for binary outcomes.

Journal: The Journal of thoracic and cardiovascular surgery
PMID: 39243960

Abstract

BACKGROUND: The use of machine learning (ML) in cardiovascular and thoracic surgery is evolving rapidly. Maximizing the capabilities of ML can help improve patient risk stratification and clinical decision making, improve accuracy of predictions, and improve resource utilization in cardiac surgery. The many nuances and intricacies of ML modeling need to be understood to appropriately implement these technologies in the clinical research setting. This primer provides an educational framework of ML for generating predicted probabilities in clinical research and illustrates it with a real-world clinical example.

Authors

  • Steven J Staffa
    Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, USA.
  • Krystof Stanek
    Department of Plastic and Oral Surgery, Boston Children's Hospital, Harvard Medical School, Boston, Mass.
  • Viviane G Nasr
    Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Mass.
  • David Zurakowski
    From the Department of Radiology, New York University Langone Health, 301 E 17th St, New York, NY 10010 (J.R.Z.); Departments of Radiology (L.S., D.J.) and Orthopedic Surgery (K.A.R.), Columbia University Irving Medical Center, New York, NY; and Departments of Anesthesiology (S.S., D.Z.), Surgery (S.S., D.Z.), and Radiology (A.T.), Boston Children's Hospital, Harvard Medical School, Boston, Mass.

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