Using machine learning to predict one-year cardiovascular events in patients with severe dilated cardiomyopathy.

Journal: European journal of radiology
Published Date: Jun 11, 2019

Abstract

PURPOSE: Dilated cardiomyopathy (DCM) is a common form of cardiomyopathy and it is associated with poor outcomes. A poor prognosis of DCM patients with low ejection fraction has been noted in the short-term follow-up. Machine learning (ML) could aid clinicians in risk stratification and patient management after considering the correlation between numerous features and the outcomes. The present study aimed to predict the 1-year cardiovascular events in patients with severe DCM using ML, and aid clinicians in risk stratification and patient management.

Authors

  • Rui Chen
    College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shanxi, China.
  • Aijia Lu
    Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Jingjing Wang
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, China; School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, China.
  • Xiaohai Ma
    Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Lei Zhao
    Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.
  • Wanjia Wu
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, China.
  • Zhicheng Du
    Department of Medical Statistics and Epidemiology & Health Information Research Center & Guangdong Key Laboratory of Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
  • Hongwen Fei
    Department of Cardiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, China.
  • Qiongwen Lin
    Department of Cardiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, China.
  • Zhuliang Yu
    College of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong Province, China. Electronic address: zlyu@scut.edu.cn.
  • Hui Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Keywords

External Resources

Popular Topics
Recent Journals