Predicting prognostic factors in kidney transplantation using a machine learning approach to enhance outcome predictions: a retrospective cohort study.

Journal: International journal of surgery (London, England)
PMID: 39116448

Abstract

BACKGROUND: Accurate forecasting of clinical outcomes after kidney transplantation is essential for improving patient care and increasing the success rates of transplants. The authors' study employs advanced machine learning (ML) algorithms to identify crucial prognostic indicators for kidney transplantation. By analyzing complex datasets with ML models, the authors aim to enhance prediction accuracy and provide valuable insights to support clinical decision-making.

Authors

  • Jin-Myung Kim
    Division of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
  • HyoJe Jung
    Department of Information Medicine, Asan Medical Center.
  • Hye Eun Kwon
    Division of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine.
  • Youngmin Ko
    Department of Statistics and Data Science, Northwestern University, Chicago, USA.
  • Joo Hee Jung
    Division of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine.
  • Hyunwook Kwon
    Division of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine.
  • Young Hoon Kim
    Department of Surgery, College of Medicine, Ulsan University, Asan Medical Center, Seoul, Korea.
  • Tae Joon Jun
  • Sang-Hyun Hwang
    Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Sung Shin
    Division of Kidney and Pancreas Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine; sshin@amc.seoul.kr.

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