Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure.

Journal: ESC heart failure
Published Date: Sep 28, 2021

Abstract

AIMS: Predicting the risk of malignant arrhythmias (MA) in hospitalized patients with heart failure (HF) is challenging. Machine learning (ML) can handle a large volume of complex data more effectively than traditional statistical methods. This study explored the feasibility of ML methods for predicting the risk of MA in hospitalized HF patients.

Authors

  • Qi Wang
    Biotherapeutics Discovery Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Bin Li
    Department of Magnetic Resonance Imaging (MRI), Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Kangyu Chen
    Heart Failure Center, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
  • Fei Yu
    Department of Nutrition and food hygiene, College of Public Health of Zhengzhou University, Zhengzhou, China, 450001. Electronic address: 53615631@qq.com.
  • Hao Su
    1 Department of Urology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.
  • Kai Hu
    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
  • Zhiquan Liu
    Heart Failure Center, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
  • Guohong Wu
    Heart Failure Center, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
  • Ji Yan
    Heart Failure Center, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.
  • Guohai Su
    Division of Cardiology, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, No. 105 Jiefang Road, Jinan, 250013, China.

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