Development and validation of an interpretable machine learning model for predicting acute kidney injury after pancreatic surgery

doi:10.3748/wjg.v32.i19.116271

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World Journal of Gastroenterology

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1007-9327

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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 Gastroenterol. May 21, 2026; 32(19): 116271
Published online May 21, 2026. doi: 10.3748/wjg.v32.i19.116271

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Figure 5 Decision curves of different models. A: Developing cohort; B: Validation cohort. CatBoost: Categorical boosting; KNN: K-nearest neighbor; LightGBM: Light gradient boosting machine; SVM: Support vector machine; XGBoost: Extreme gradient boosting with classification trees.

Citation: Lin C, Fu RK, Zheng H, Li TY, Han JS, Margonis GA, Wang JJ, Dong LB, Wang NS, Sun YX, Wang YZ, Liu C, Xu Q, Han XL, Zhang TP, Guo JC, Dai MH, Xia P, Chen LM, Wang WB. Development and validation of an interpretable machine learning model for predicting acute kidney injury after pancreatic surgery. World J Gastroenterol 2026; 32(19): 116271
URL: https://www.wjgnet.com/1007-9327/full/v32/i19/116271.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i19.116271

Lin C, Fu RK, Zheng H, Li TY, Han JS, Margonis GA, Wang JJ, Dong LB, Wang NS, Sun YX, Wang YZ, Liu C, Xu Q, Han XL, Zhang TP, Guo JC, Dai MH, Xia P, Chen LM, Wang WB. Development and validation of an interpretable machine learning model for predicting acute kidney injury after pancreatic surgery. World J Gastroenterol 2026; 32(19): 116271 [DOI: 10.3748/wjg.v32.i19.116271]

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