  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 at higher risk of
death. In this study, we use two retrospective cohorts extracted from the eICU
and MIMIC-IV databases, to develop a novel pseudo-dynamic machine learning
framework for mortality prediction in the ICU with interpretability and
clinical risk analysis. The method provides accurate prediction for ICU
patients up to 24 hours before the event and provide time-resolved
interpretability results. 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) for 6-hour prediction of mortality 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
