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This study presents XMI-ICU, an explainable machine learning framework for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data from two US-based ICU databases. The framework demonstrates superior predictive performance with AUROCs of 92.0 for 6-hour mortality predictions and maintains reliability across various time horizons while providing time-resolved interpretability through Shapley values. XMI-ICU outperforms traditional risk assessment tools and offers valuable clinica

Author

marakibsarker2001

Language

EN