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 1 Correlation heatmap of variables. A: Correlation heatmap of different variables; B: Heatmap of significantly different variables. Cr: Creatinine; LAC: Lactate; FFP: Fresh frozen plasma; ICU: Intensive care unit; PRS: Postreperfusion syndrome; AST: Aspartate aminotransferase; TB: Total bilirubin; AKI: Acute kidney injury; Ca²⁺: Calcium ion; Na⁺: Sodium ion.

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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