Discover important donor-recipient risk factors and interactions in heart transplant primary graft dysfunction with machine learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jul 1, 2025

Abstract

OBJECTIVES: Primary graft dysfunction (PGD) is an essential outcome after the heart transplant, which causes severe complications and symptoms for recipients. The in advance prediction of PGD can help the transplant physician better manage the risks of PGD occurrence for patients. Domain experts have identified some important risk factors leading to PGD. However, a widely accepted PGD prediction method is lacking from a computational perspective. In this work, we focus on the prediction of PGD after heart transplant with machine learning (ML).

Authors

  • Sirui Ding
    Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, United States.
  • Yafen Liang
    Department of Anesthesiology, University of Texas Health Center at Houston, Houston, TX 77030, United States.
  • Chia-Yuan Chang
    Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77840, United States.
  • Cheryl Brown
    Department of Political Science and Public Administration, University of North Carolina at Charlotte, Charlotte, NC 28223, United States.
  • Xiaoqian Jiang
    School of Biomedical Informatics, University of Texas Health, Science Center at Houston, Houston, TX, USA.
  • Xia Hu
    Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA.
  • Na Zou
    Department of Industrial Engineering, University of Houston, Houston, TX 77204, United States.

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