The Prognostic Performance of Artificial Intelligence and Machine Learning Models for Mortality Prediction in...

In-hospital mortality prediction for patients admitted to the ICU remains a critical challenge in the field of critical care medicine. This systematic review evaluates the application of artificial intelligence (AI) and machine learning (ML) models for predicting in-hospital mortality in ICU settings. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed 15 studies published between January 2015 and April 2025 that utilized AI and ML approaches for mortality prediction in ICU populations. The most commonly employed algorithms were extreme gradient boosting (XGBoost), random forest, and logistic regression, with data predominantly sourced from two major publicly available critical care databases: the Medical Information Mart for Intensive Care (MIMIC) and the eICU Collaborative Research Database (eICU-CRD). Across all studies, AI and ML models consistently outperformed traditional clinical scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE), Sequential Organ Failure Assessment (SOFA), and Simplified Acute Physiology Score (SAPS), demonstrating superior discriminative performance in mortality prediction. Ensemble methods, particularly random forest and XGBoost, generally achieved the highest predictive accuracy, while deep learning approaches such as recurrent neural networks showed particular promise for analyzing temporal trends in physiological data. Key predictive features identified across multiple studies included patient age, vital signs (especially heart rate and blood pressure), laboratory values (particularly markers of renal function), and metrics of neurological status such as level of consciousness. Importantly, several studies developed models that required only routinely collected clinical data available within the first 24 hours of ICU admission, demonstrating the feasibility of early risk stratification using AI and ML. Although most research remains retrospective in nature and confined to a limited number of datasets, the consistent performance advantages observed across diverse modeling approaches underscore the significant clinical potential of AI and ML in ICU mortality prediction. Future research should prioritize the use of standardized model development and reporting methodologies, prospective validation in diverse and real-world clinical settings, exploration of integration and implementation challenges, and rigorous assessments of clinical impact. Such efforts are critical to translating these promising predictive technologies into improved decision-making processes and outcomes for critically ill patients.