A novel approach to dry weight adjustments for dialysis patients using machine learning.

Journal: PloS one
PMID: 33891656

Abstract

BACKGROUND AND AIMS: Knowledge of the proper dry weight plays a critical role in the efficiency of dialysis and the survival of hemodialysis patients. Recently, bioimpedance spectroscopy(BIS) has been widely used for set dry weight in hemodialysis patients. However, BIS is often misrepresented in clinical healthy weight. In this study, we tried to predict the clinically proper dry weight (DWCP) using machine learning for patient's clinical information including BIS. We then analyze the factors that influence the prediction of the clinical dry weight.

Authors

  • Hae Ri Kim
    Department of Dental Science, Graduate School, Kyungpook National University, Daegu 700-412, Korea. harry@knu.ac.kr.
  • Hong Jin Bae
    Division of Nephrology, Department of Internal Medicine, Cheongju St. Mary's Hospital, Cheongju, South Korea.
  • Jae Wan Jeon
    Division of Nephrology, Department of Internal Medicine, Chungnam National University Sejong Hospital, Sejong, South Korea.
  • Young Rok Ham
    Department of Nephrology, Medical School, Chungnam National University, Daejeon, South Korea.
  • Ki Ryang Na
    Department of Nephrology, Medical School, Chungnam National University, Daejeon, South Korea.
  • Kang Wook Lee
    Department of Nephrology, Medical School, Chungnam National University, Daejeon, South Korea.
  • Yun Kyong Hyon
    Division of Medical Mathematics, National Institute for Mathematical Sciences, 70 Yuseong-daero 1689beon-gil, Yuseong-gu, Daejeon, Republic of Korea, 34047.
  • Dae Eun Choi
    Department of Nephrology, Medical School, Chungnam National University, Daejeon, South Korea.

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