Application of machine learning models in predicting the risk of thromboembolic events in patients with nonvariceal gastrointestinal bleeding

doi:10.3748/wjg.v32.i3.115527

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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

Retrospective Cohort Study

World J Gastroenterol. Jan 21, 2026; 32(3): 115527
Published online Jan 21, 2026. doi: 10.3748/wjg.v32.i3.115527

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Figure 3 The calibration curve of different machine learning models. A: Categorical boosting, L1 regularized logistic regression; B: Random forest for predicting thromboembolic events showed good performance. CatBoost: Categorical boosting; LassoLR: L1 regularized logistic regression; SVM: Support vector machines; XGBoost: Extreme gradient boosting.

Citation: Lu C, Cheng HY, Zhu RK, Zhou YD, Sun KF, Xu L, Sang JZ, Chen JE, Yu CH, Qin YL, Li L. Application of machine learning models in predicting the risk of thromboembolic events in patients with nonvariceal gastrointestinal bleeding. World J Gastroenterol 2026; 32(3): 115527
URL: https://www.wjgnet.com/1007-9327/full/v32/i3/115527.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i3.115527

Lu C, Cheng HY, Zhu RK, Zhou YD, Sun KF, Xu L, Sang JZ, Chen JE, Yu CH, Qin YL, Li L. Application of machine learning models in predicting the risk of thromboembolic events in patients with nonvariceal gastrointestinal bleeding. World J Gastroenterol 2026; 32(3): 115527 [PMID: 41640609 DOI: 10.3748/wjg.v32.i3.115527]

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