Predictive Value of Machine Learning for the Risk of In-Hospital Death in Patients With Heart Failure: A Systematic Review and Meta-Analysis.

Journal: Clinical cardiology
PMID: 39723651

Abstract

BACKGROUND: The efficiency of machine learning (ML) based predictive models in predicting in-hospital mortality for heart failure (HF) patients is a topic of debate. In this context, this study's objective is to conduct a meta-analysis to compare and assess existing prognostic models designed for predicting in-hospital mortality in HF patients.

Authors

  • Liyuan Yan
    Department of Cardiology, Affiliated Changshu Hospital of Nantong University, Changshu, Jiangsu, China.
  • Jinlong Zhang
    Tianjin Institute of Animal Sciences, Tianjin, China.
  • Le Chen
    School of Pharmacy, Hubei University of Chinese Medicine, Wuhan, 430065, China; College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China. Electronic address: chenle9169@163.com.
  • Zongcheng Zhu
    Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Xiaodong Sheng
    Department of Cardiology, Affiliated Changshu Hospital of Nantong University, Changshu, Jiangsu, China.
  • Guanqun Zheng
    Department of Cardiology, Affiliated Changshu Hospital of Nantong University, Changshu, Jiangsu, China.
  • Jiamin Yuan
    Health construction administration center, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China.

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