Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning.

Journal: Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Heart failure (HF) is a major driver of global morbidity and mortality. Early identification of patients at risk remains challenging due to complex, multivariate clinical relationships. Machine learning (ML) methods offer promise for more accurate prognostication.

Authors

  • Jia Liu
    Department of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China.
  • Dan Zhu
    School of Pharmacy, Minzu University of China, Beijing 100081, China.
  • Lingzhi Deng
    Department of Cardiology, Zone 6, The First People's Hospital of Chenzhou, Chenzhou, HuNan, China.
  • Xiaoliang Chen
    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi 710049, P.R. China.

Keywords

External Resources

Popular Topics
Recent Journals