Machine learning-based prediction of postoperative mortality risk after abdominal surgery

doi:10.4240/wjgs.v17.i4.103696

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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

Retrospective Study

World J Gastrointest Surg. Apr 27, 2025; 17(4): 103696
Published online Apr 27, 2025. doi: 10.4240/wjgs.v17.i4.103696

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Figure 4 Decision-tree prediction model for the risk of postoperative death in patients who underwent abdominal surgery. The independent influencing factors used in the process of establishing the model were derived from the multivariate logistic regression analysis. APTT: Activated partial thromboplastin time; WBC: White blood cell.

Citation: Yuan JH, Jin YM, Xiang JY, Li SS, Zhong YX, Zhang SL, Zhao B. Machine learning-based prediction of postoperative mortality risk after abdominal surgery. World J Gastrointest Surg 2025; 17(4): 103696
URL: https://www.wjgnet.com/1948-9366/full/v17/i4/103696.htm
DOI: https://dx.doi.org/10.4240/wjgs.v17.i4.103696

Yuan JH, Jin YM, Xiang JY, Li SS, Zhong YX, Zhang SL, Zhao B. Machine learning-based prediction of postoperative mortality risk after abdominal surgery. World J Gastrointest Surg 2025; 17(4): 103696 [PMID: 40291872 DOI: 10.4240/wjgs.v17.i4.103696]

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