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915 for AMI 1 month and 0.999 for all-cause mortality 1 month. The random forest model had better predictive accuracy than logistic regression, SVC, and KNN. We further integrated the AI prediction model with the HIS to assist physicians with decision-making in real time. Validation of the AI prediction model by new patients showed AUCs of 0.907 for AMI 1 month and 0.888 for all-cause mortality 1 month. An AI real-time prediction model is a promising method for assisting physicians in predicting MACE in ED patients
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