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Heart attack remain one of the greatest contributors to mortality in theUnited States and globally. Patients admitted to the intensive care unit (ICU)with diagnosed heart attack (myocardial infarction or MI) are at higher risk ofdeath. In this study, we use two retrospective cohorts extracted from the eICUand MIMIC-IV databases, to develop a novel pseudo-dynamic machine learningframework for mortality prediction in the ICU with interpretability andclinical risk analysis. The method provides accurate prediction for ICUpatients up to 24 hours before the event and provide time-resolvedinterpretability results. The performance of the framework relying on extremegradient boosting was evaluated on a held-out test set from eICU, andexternally validated on the MIMIC-IV cohort using the most important featuresidentified by time-resolved Shapley values achieving AUCs of 91.0 (balancedaccuracy of 82.3) for 6-hour prediction of mortality respectively. We show thatour framework successfully leverages time-series physiological measurements bytranslating them into stacked static prediction problems to be robustlypredictive through time in the ICU stay and can offer clinical insight fromtime-resolved interpretability

XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients