摘要Objective The study aimed to develop machine learning(ML)models to predict the mortality of patients with acute gastro-intestinal bleeding(AGIB)in the intensive care unit(ICU)and compared their prognostic performance with that of Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE-Ⅱ)score.Methods A total of 961 AGIB patients admitted to the ICU of Renmin Hospital of Wuhan University from January 2020 to December 2023 were enrolled.Patients were randomly divided into the training cohort(n=768)and the validation cohort(n=193).Clinical data were collected within the first 24 h of ICU admission.ML models were constructed using Python V.3.7 package,employing 3 different algorithms:XGBoost,Random Forest(RF)and Gradient Boosting Decision Tree(GBDT).The area under the receiver operating characteristic(ROC)curve(AUC)was used to evaluate the performance of different models.Results A total of 94 patients died with an overall mortality of 9.78％(11.32％in the training cohort and 8.96％in the vali-dation cohort).Among the 3 ML models,the GBDT algorithm demonstrated the highest predictive performance,achieving an AUC of 0.95(95％CI 0.90-0.99),while the AUCs of XGBoost and RF models were 0.89(95％CI 0.82-0.96)and 0.90(95％CI 0.84-0.96),respectively.In comparison,the APACHE-Ⅱ model achieved an AUC of 0.74(95％CI 0.69-0.87),with a specificity of 70.97％(95％CI 64.07-77.01).When APACHE-Ⅱ score was incorporated into the GBDT algorithm,the ensemble model achieved an AUC of 0.98(95％CI 0.96-0.99)with a sensitivity of 85.71％and a specificity up to 95.15％.Conclusions The GBDT model serves as a reliable tool for accurately predicting the in-hospital mortality for AGIB patients.When integrated with the APACHE-Ⅱ score,the ensemble GBDT algorithm further enhances predictive accuracy and pro-vides valuable insights for prognostic evaluation.