Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.

Journal: European journal of clinical investigation
PMID: 39660499

Abstract

BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombotic events still exists. This study aimed to construct machine learning (ML) models to predict the residual risk in these patients.

Authors

  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Ivan Olier
    1Manchester Metropolitan University, Manchester, UK.
  • Sandra Ortega-Martorell
    School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.
  • Bi Huang
    School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China.
  • Hironori Ishiguchi
    Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
  • Ho Man Lam
    Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University, and Liverpool Heart & Chest Hospital, Liverpool, UK.
  • Kui Hong
    Department of Cardiovascular Medicine, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
  • Menno V Huisman
    Department of Medicine - Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, The Netherlands.
  • Gregory Y H Lip
    Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L69 3BX Liverpool, UK.

Keywords

External Resources

Popular Topics
Recent Journals