An artificial intelligence (AI) real-time prediction model is a promising method for assisting physicians in predicting major adverse cardiac events (MACE) in ED patients with chest pain. Further studies to evaluate the impact on clinical practice are warranted.

Predicting MACE using the random forest model produced areas under the curves (AUC) of 0.915 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. In total, 85,254 ED patients with chest pain in three hospitals between 2009 and 2018 were identified. We randomized the patients into a 70%/30% split for ML model training and testing.

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