Interpretable machine learning models for the prediction of all-cause mortality and time to death in hemodialysis patients.

Journal: Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy
PMID: 39327762

Abstract

INTRODUCTION: The elevated mortality and hospitalization rates among hemodialysis (HD) patients underscore the necessity for the development of accurate predictive tools. This study developed two models for predicting all-cause mortality and time to death-one using a comprehensive database and another simpler model based on demographic and clinical data without laboratory tests.

Authors

  • Minjie Chen
    Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Youbing Zeng
    School of Biomedical Engineering, Sun Yat-sen University, 518107, Guangdong, China. Electronic address: zengyb6@mail2.sysu.edu.cn.
  • Mengting Liu
    Department of Ophthalmology, The Second Xiangya Hospital, Hunan Clinical Research Centre of Ophthalmic Disease, Central South University, Changsha, Hunan, China.
  • Zhenghui Li
    Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
  • Jiazhen Wu
    Depeartment of Electronic Engineering, Shantou University, Shantou, China.
  • Xuan Tian
    Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Yunuo Wang
    Discipline of Medical Imaging Sciences, The University of Sydney, Lidcombe, New South Wales, Australia.
  • Yuanwen Xu
    Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

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