Interpretable machine learning model for early complication prediction after split liver transplantation

doi:10.3748/wjg.v31.i47.114370

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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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World J Gastroenterol. Dec 21, 2025; 31(47): 114370
Published online Dec 21, 2025. doi: 10.3748/wjg.v31.i47.114370

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Figure 4 Survival of split liver transplantation recipients with four indexes. Kaplan-Meier survival curves were compared using the log-rank test. A: Systemic immune inflammatory; B: Blood loss; C: Meld scores; D: Partial lobectomy of segment IV. SLT: Split liver transplantation; SII: Systemic immune inflammatory; PL: Partial lobectomy; WPL: Without partial lobectomy; MELD: Model for end-stage liver disease.

Citation: Wang D, Zhang JY, Xie Y, Zhang KN, Jiang WT. Interpretable machine learning model for early complication prediction after split liver transplantation. World J Gastroenterol 2025; 31(47): 114370
URL: https://www.wjgnet.com/1007-9327/full/v31/i47/114370.htm
DOI: https://dx.doi.org/10.3748/wjg.v31.i47.114370

Wang D, Zhang JY, Xie Y, Zhang KN, Jiang WT. Interpretable machine learning model for early complication prediction after split liver transplantation. World J Gastroenterol 2025; 31(47): 114370 [PMID: 41479751 DOI: 10.3748/wjg.v31.i47.114370]

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