Predicting dry weight change in Hemodialysis patients using machine learning.

Journal: BMC nephrology
PMID: 37386392

Abstract

BACKGROUND: Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest classifier is a Machine Learning method that can generate high accuracy and interpretability in the data analysis of various diseases. We attempted to apply Machine Learning to adjust dry weight, the appropriate volume status of patients undergoing hemodialysis, which requires a complex decision-making process considering multiple indicators and the patient's physical conditions.

Authors

  • Hiroko Inoue
    Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
  • Megumi Oya
    Department of Epidemiology and Environmental Health, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Masashi Aizawa
    Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
  • Kyogo Wagatsuma
    Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan.
  • Masatomo Kamimae
    Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chuo- ku, Chiba, Japan.
  • Yusuke Kashiwagi
    Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
  • Masayoshi Ishii
    Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
  • Hanae Wakabayashi
    Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
  • Takayuki Fujii
    Department of Nephrology, Seirei Sakura Citizen hospital, Sakura, Chiba, Japan.
  • Satoshi Suzuki
    Department of Infectious Diseases, Tohoku University Graduate School of Medicine 2-1, Seiryo-machi, Aoba-ku Sendai Miyagi 980-8575 Japan.
  • Noriyuki Hattori
    Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chiba, 260-8677, Japan.
  • Narihito Tatsumoto
    Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan.
  • Eiryo Kawakami
    Medical Sciences Innovation Hub Program, RIKEN, Yokohama, Kanagawa, Japan.
  • Katsuhiko Asanuma
    Department of Nephrology, Graduate School of Medicine, Chiba University, Chuo-ku, Chiba, Japan. kasanuma@chiba-u.jp.

Keywords

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