A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions.

Journal: The British journal of radiology
PMID: 34464552

Abstract

OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stratification for these high-risk patients by incorporating various types of data. The aim of this study was to build an ML model to predict adverse events including all-cause deaths and heart transplantation in DCM patients with severely impaired LV systolic function.

Authors

  • Shenglei Shu
    Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Ziming Hong
    School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
  • Qinmu Peng
    School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China; Shenzhen Huazhong University of Science and Technology Research Institute, China. Electronic address: pengqinmu@hust.edu.cn.
  • Xiaoyue Zhou
    MR Collaboration, Siemens Healthineers, Shanghai, China.
  • Tianjng Zhang
    Philips Healthcare, Guangzhou, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Chuansheng Zheng

Keywords

External Resources

Popular Topics
Recent Journals