Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning.

Journal: ESC heart failure
PMID: 39168476

Abstract

AIMS: Certain critical risk factors of heart failure with preserved ejection fraction (HFpEF) patients were significantly different from those of heart failure with reduced ejection fraction (HFrEF) patients, resulting in the limitations of existing predictive models in real-world situations. This study aimed to develop a machine learning model for predicting 90 day readmission for HFpEF patients.

Authors

  • Baojia Zheng
    The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Tao Liang
    Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, 21201, USA.
  • Jianping Mei
    The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Xiuru Shi
    The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Xiaohui Liu
    Science and Technology on Parallel and Distributed Laboratory, Changsha, China.
  • Sikai Li
    The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Yuting Wan
    The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Yifeng Zheng
    The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Xiaoyue Yang
    The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Yanxia Huang
    The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.

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