Heart attack remain one of the greatest contributors to mortality in the United States and globally. Patients admitted to the intensive care unit (ICU) with diagnosed heart attack (myocardial infarction or MI) are more likely to suffer a secondary episode of MI and are at higher risk of death. In this study, we use two retrospective cohorts extracted from the eICU and MIMIC-IV databases, to develop a pseudo-dynamic machine learning framework for mortality and recurrent heart attack prediction in the ICU with interpretability and clinical risk analysis. The method provides accurate prediction of both outcomes for ICU patients up to 24 hours before the event and provide time-resolved interpretability results based on dynamic extraction of features from noisy and sparse time-series signals. The performance of the framework relying on extreme gradient boosting was evaluated on a held-out test set from eICU, and externally validated on the MIMIC-IV cohort using the most important features identified by time-resolved Shapley values achieving AUCs of 91.0 (balanced accuracy of 82.3) and 85.6 (balanced accuracy of 74.5) for 6-hour prediction of mortality and recurrent heart attack respectively. We show that our framework successfully leverages time-series physiological measurements by translating them into stacked static prediction problems to be robustly predictive through time in the ICU stay and can offer clinical insight from time-resolved interpretability.