Prediction of post-donation renal function using machine learning techniques and conventional regression models in living kidney donors.

Journal: Journal of nephrology
PMID: 39073700

Abstract

BACKGROUND: Accurate prediction of renal function following kidney donation and careful selection of living donors are essential for living-kidney donation programs. We aimed to develop a prediction model for post-donation renal function following living kidney donation using machine learning.

Authors

  • Junseok Jeon
    Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Yeejun Song
    Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
  • Jae Yong Yu
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.
  • Weon Jung
    Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
  • Kyungho Lee
    Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Jung Eun Lee
    Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Wooseong Huh
    Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Won Chul Cha
    Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Hye Ryoun Jang
    Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea. shinehr@skku.edu.

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