A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR.

Journal: BMC medical informatics and decision making
PMID: 39901121

Abstract

BACKGROUND: There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm for identification of paroxysmal and persistent AF, and investigate the influencing factors.

Authors

  • Yuqi Zhang
    State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing, China.
  • Sijin Li
    Shanxi Medical University, Taiyuan 030009, PR China; Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China. Electronic address: lisjnm123@163.com.
  • Peibiao Mai
    Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences (Shenzhen Sun Yat-Sen Cardiovascular Hospital), Shenzhen, China.
  • Yanqi Yang
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
  • Niansang Luo
    Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
  • Chao Tong
    School of Computer Science and Engineering, Beihang, Beijing, 100191, China.
  • Kuan Zeng
    Department of Cardiovascular Surgery, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China. zengkuan3@mail.sysu.edu.cn.
  • Kun Zhang
    Philosophy Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

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