PREDICTIVE MODELING OF ORGAN DYSFUNCTION IN SEPSIS USING INTEGRATED CLINICAL AND LABORATORY PARAMETERS

Abstract

Sepsis remains a leading cause of morbidity and mortality worldwide, largely due to challenges in early diagnosis, heterogeneous clinical presentation, and rapid disease progression. This study investigated the application of advanced artificial intelligence and machine learning techniques for early sepsis prediction, organ dysfunction forecasting, and mortality risk stratification in intensive care unit settings. A mixed experimental methodology was employed, integrating large-scale multimodal clinical data comprising continuous vital signs, laboratory biomarkers, and electronic health record information. Multiple machine learning architectures, including recurrent, ensemble, and attention-based models, were developed and evaluated using comprehensive performance metrics.The results demonstrated that artificial intelligence–based models significantly outperformed traditional approaches, achieving high discriminatory power with AUROC values exceeding conventional thresholds, improved sensitivity–specificity balance, and clinically meaningful lead times of several hours prior to sepsis onset. Transformer and stacked ensemble models showed superior performance in early warning capability, mortality risk stratification, and organ dysfunction prediction. Robustness and calibration analyses confirmed model stability under noisy physiological signals and across external validation cohorts. Visual analytics further illustrated clear temporal risk escalation patterns and high-dimensional separability between septic and non-septic patient trajectories. Importantly, alert distribution analyses indicated reduced false-positive rates, supporting clinical usability.Overall, the study demonstrates that artificial intelligence–driven predictive modeling offers a reliable, interpretable, and clinically actionable approach to sepsis management. These findings support the integration of AI-based decision support systems into critical care workflows to enable earlier intervention, personalized treatment strategies, and improved patient outcomes.