An explainable machine learning model to predict early and late acute kidney injury after major hepatectomy.

Journal: HPB : the official journal of the International Hepato Pancreato Biliary Association
PMID: 38705794

Abstract

BACKGROUND: Risk assessment models for acute kidney injury (AKI) after major hepatectomy that differentiate between early and late AKI are lacking. This retrospective study aimed to create a model predicting AKI through machine learning and identify features that contribute to the development of early and late AKI.

Authors

  • Seokyung Shin
    Department of Anesthesiology and Pain Medicine, Severance Hospital, Anesthesia and Pain Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodamun-gu, Seoul 03722, South Korea.
  • Tae Y Choi
    Department of Anesthesiology and Pain Medicine, Severance Hospital, Anesthesia and Pain Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodamun-gu, Seoul 03722, South Korea.
  • Dai H Han
    Department of Surgery, Division of Hepato-biliary and Pancreatic Surgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodamun-gu, Seoul 03722, South Korea.
  • Boin Choi
    Severance Hospital, 50-1 Yonsei-ro, Seodamun-gu, Seoul 03722, South Korea.
  • Eunsung Cho
    Severance Hospital, 50-1 Yonsei-ro, Seodamun-gu, Seoul 03722, South Korea.
  • Yeong Seog
    Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodamun-gu, Seoul 03722, South Korea.
  • Bon-Nyeo Koo
    Department of Anesthesiology and Pain Medicine, Severance Hospital, Anesthesia and Pain Research Institute, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodamun-gu, Seoul 03722, South Korea. Electronic address: koobn@yuhs.ac.

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