Machine learning models for predicting acute kidney injury after pediatric living donor liver transplantation in biliary atresia

doi:10.4240/wjgs.v18.i7.120759

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World Journal of Gastrointestinal Surgery

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1948-9366

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Surg. Jul 27, 2026; 18(7): 120759
Published online Jul 27, 2026. doi: 10.4240/wjgs.v18.i7.120759

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Figure 6 The influence of each independent factor on the prediction of acute kidney injury after living donor liver transplantation. A: The influence of pre- creatinine on acute kidney injury (AKI) prediction; B: The influence of post-aspartate aminotransferase on AKI prediction; C: The influence of anhepatic phase lactate level on AKI prediction; D: The influence of anhepatic phase calcium ions on AKI prediction; E: The influence of fresh frozen plasma infusion situation on AKI prediction; F: The influence of gender on AKI prediction. SHAP: SHapley Additive exPlanations; Cr: Creatinine; AST: Aspartate aminotransferase; LAC: Lactate; Ca²⁺: Calcium ion; FFP: Fresh frozen plasma.

Citation: Wang RR, Zhu M, Ren HC, Yu WL. Machine learning models for predicting acute kidney injury after pediatric living donor liver transplantation in biliary atresia. World J Gastrointest Surg 2026; 18(7): 120759
URL: https://www.wjgnet.com/1948-9366/full/v18/i7/120759.htm
DOI: https://dx.doi.org/10.4240/wjgs.v18.i7.120759

Wang RR, Zhu M, Ren HC, Yu WL. Machine learning models for predicting acute kidney injury after pediatric living donor liver transplantation in biliary atresia. World J Gastrointest Surg 2026; 18(7): 120759 [DOI: 10.4240/wjgs.v18.i7.120759]

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