Machine learning-based model for worsening heart failure risk in Chinese chronic heart failure patients.

Journal: ESC heart failure
PMID: 39243185

Abstract

AIMS: This study aims to develop and validate an optimal model for predicting worsening heart failure (WHF). Multiple machine learning (ML) algorithms were compared, and the results were interpreted using SHapley Additive exPlanations (SHAP). A clinical risk calculation tool was subsequently developed based on these findings.

Authors

  • Ziyi Sun
    Graduate School, Beijing University of Chinese Medicine, Beijing, China.
  • Zihan Wang
    Graduate School, Beijing University of Chinese Medicine, Beijing, China.
  • Zhangjun Yun
    Graduate School, Beijing University of Chinese Medicine, Beijing, China.
  • Xiaoning Sun
    Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China.
  • Jianguo Lin
    Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Xiaoxiao Zhang
    Key Laboratory of Drug Quality Control&Pharmacovigilance (China Pharmaceutical University), Ministry of Education, Nanjing, China.
  • Qingqing Wang
  • Jinlong Duan
    Key Laboratory of Grain Information Processing and Control, Henan University of Technology, Ministry of Education, Zhengzhou, 450001, China.
  • Li Huang
    National Research Center for Resettlement (NRCR), Hohai University, 1 Xikang Road, Nanjing 210098, China. lily8214@hhu.edu.cn.
  • Lin Li
    Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany.
  • Kuiwu Yao
    Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.

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