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

doi:10.3748/wjg.v32.i19.116271

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 32, Issue 19

This Article

(25)

(31)

(0)

(38)

(2)

(604)

Peer-Review Report of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Journal Information of This Article

Publication Name

World Journal of Gastroenterology

ISSN

1007-9327

Publisher of This Article

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

Retrospective Study

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

Full Size Figure Download Figure

Figure 6 SHapley Additive exPlanations summary plot for the categorical boosting model. A: Average absolute impact of variables on the final model output magnitude ordered by decreasing feature importance; B: Beeswarm plot of the final model. A footnote of 0 (such as variable 0) means the preoperative value while a footprint of 1 (such as variable 1) means the postoperative value. SCr: Serum creatinine; ICU: Intensive care unit; WBC: White blood cell; RBC: Red blood cell; TBil: Total bilirubin; PT: Prothrombin time; APTT: Activated partial thromboplastin time; Alb: Albumin; NSAIDs: Nonsteroidal anti-inflammatory drugs; ACEI: Angiotensin-converting enzyme inhibitors; ARB: Angiotensin receptor blockers; POD: Postoperative day; SHAP: SHapley Additive exPlanations.

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]

All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.