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

翻译：心脏病发作仍是美国及全球范围内死亡率的主要贡献因素之一。被诊断为心脏病发作（急性心肌梗死或MI）而入住重症监护室（ICU）的患者死亡风险更高。本研究利用从eICU和MIMIC-IV数据库中提取的两个回顾性队列，开发了一种新颖的伪动态机器学习框架，用于ICU死亡率预测，兼具可解释性与临床风险分析能力。该方法可在事件发生前24小时内为ICU患者提供准确预测，并输出时间分辨的可解释性结果。基于极限梯度提升的框架性能在eICU的保留测试集上进行了评估，并在MIMIC-IV队列上利用时间分辨夏普利值识别的最重要特征进行了外部验证，在6小时死亡率预测中分别实现了91.0的AUC（平衡准确率82.3）。我们证明，该框架通过将时间序列生理测量值转化为堆叠式静态预测问题，成功实现了在ICU住院期间随时间变化的稳健预测能力，并能从时间分辨的可解释性结果中提供临床洞见。