MACHINE LEARNING MODELS FOR PREDICTING PATIENT OUTCOMES IN CRITICAL CARE

Abstract

Predicting adverse outcomes in intensive care units (ICUs)—including in-hospital mortality, prolonged length of stay, and unplanned readmission—can inform timely interventions and resource allocation. This article reviews and synthesizes state-of-the-art machine learning (ML) methods for outcome prediction using electronic health records (EHR), physiologic time series, and laboratory data. We outline robust cohort construction from publicly available critical-care databases, compare feature engineering strategies (static vs. time-varying, handcrafted vs. learned), and detail modeling approaches from regularized generalized linear models to tree ensembles, recurrent neural networks, and transformers. We emphasize rigorous evaluation beyond discrimination (AUROC/PR-AUC) to include calibration, decision-curve analysis, and prospective transportability. Practical guidance is provided for interpretability (feature attribution, counterfactuals), fairness auditing across demographic subgroups, and MLOps for clinical deployment (drift detection, human-in-the-loop oversight). We conclude with a staged roadmap for validation—from internal cross-validation to silent shadow testing and pragmatic trials—highlighting regulatory, ethical, and implementation considerations in low- and middle-income countries (LMICs).