A lightweight graph neural network to predict long-term mortality in coronary artery disease patients: an interpretable causality-aware approach.

Journal: Journal of biomedical informatics
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Coronary artery disease (CAD) causes substantial death toll in the United States and worldwide. While traditional methods for CAD mortality prediction are based on established risk factors, they have significant limitations in accuracy, adaptability to diverse populations, performance for individual risk prediction compared to group data, and incorporation of socioeconomic and lifestyle variations. Machine learning (ML) models have demonstrated superior performance in CAD prediction; however, they often struggle with capturing complex data interactions that can impact mortality.

Authors

  • Mohammad Yaseliani
    Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA. Electronic address: mohammadyaselian@ufl.edu.
  • Md Noor-E-Alam
    Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.
  • Osama Dasa
    Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States.
  • Xiaochen Xian
    H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
  • Carl J Pepine
    Division of Cardiovascular Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL, USA.
  • Md Mahmudul Hasan
    Queensland University of Technology (QUT), Centre for Accident Research and Road Safety Queensland (CARRS-Q), Australia; Queensland University of Technology (QUT), Institute of Health and Biomedical Innovation (IHBI), Australia. Electronic address: mahmudul.hasan.eee.kuet@gmail.com.

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