Artificial intelligence and mortality prediction in acute coronary syndromes

Graphical AbstractThe process of artificial intelligence and mortality prediction using ECG data is illustrated. The 12 lead ECG data is processed to extract human-engineered features, forming the ECG risk model. Deep survival trees analyze the data through majority voting or averaging to produce a final result. This result categorizes individuals into bio-personalized risk groups using Bayesian GMM-based clustering, indicating low, moderate, and high risk of mortality.Open in new tabDownload slide