An Artificial Intelligence and Machine Learning model for Personalized Prediction of Long-term Mitral Valve Repair Durability.

Journal: The Journal of thoracic and cardiovascular surgery
Published Date: Jul 18, 2025

Abstract

OBJECTIVES: To compare Random Survival Forest (RSF), a machine learning (ML) method, with Cox Proportional Hazards (CPH) models in predicting long-term mitral valve (MV) repair durability, focusing on clinical utility and personalized decision-making.

Authors

  • Mohsyn Imran Malik
    Division of Cardiac Surgery, Department of Surgery, Western University, London Health Science Centre, London, Ontario, Canada.
  • Rashmi Nedadur
    Division of Cardiac Surgery, Schulich Heart Centre; Department of Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.
  • Michael W A Chu

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