ECG-based machine learning model for AF identification in patients with first ischemic stroke.

Journal: International journal of stroke : official journal of the International Stroke Society
PMID: 39533802

Abstract

BACKGROUND: The recurrence rate of strokes associated with atrial fibrillation (AF) can be substantially reduced through the administration of oral anticoagulants. However, previous studies have not demonstrated a clear benefit from the universal application of oral anticoagulants in patients with embolic stroke of undetermined source. Timely detection of AF remains a challenge in patients with stroke.

Authors

  • Chih-Chieh Yu
    Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei City.
  • Yu-Qi Peng
    Department of Biomedical Sciences and Engineering, National Central University, Taoyuan.
  • Chen Lin
    Faculty of Business and Economics, University of Hong Kong, Hong Kong SAR 999077, China.
  • Chia-Hsin Chiang
    Department of Biomedical Sciences and Engineering, National Central University, Taoyuan.
  • Chih-Min Liu
    Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Yenn-Jiang Lin
    Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan.
  • Lian-Yu Lin
    Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei City.
  • Men-Tzung Lo
    Department of Biomedical Sciences and Engineering, National Central University, Chung-li, Taiwan. Electronic address: mzlo@ncu.edu.tw.

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