Development and validation of machine learning predictive models for assessing dialysis adequacy in dialysis patients.

Journal: The International journal of artificial organs
Published Date: Jul 28, 2025

Abstract

PURPOSE: The assessment of dialysis adequacy is of great clinical importance. However, it depends on the nonlinear effects of numerous confounding factors and is therefore difficult to predict using traditional statistical methods. In this study, we used Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator Regression (LASSO) to assess dialysis adequacy.

Authors

  • Jie Zhou
    Departments of Ultrasound, Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences, Shanghai, China.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Linge Zhang
    Department of Nephrology, The First Affiliated Hospital of Xi'an Medical University, Xi'an Medical University, Xi'an, Shaanxi, China.
  • Qiaona Zhang
    Department of Nephrology, The First Affiliated Hospital of Xi'an Medical University, Xi'an Medical University, Xi'an, Shaanxi, China.
  • Jie Wang
  • Hang Zhao
    Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai 201203, China.
  • Yongqi Dang
    Department of Nephrology, The First Affiliated Hospital of Xi'an Medical University, Xi'an Medical University, Xi'an, Shaanxi, China.
  • Shiyu Zhang
    1Medical School of Chinese PLA, Beijing.
  • Lu Li
    State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, China.

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