TITLE:
AI-Driven Early Warning and Risk Management System for Delirium in ICU Patients
AUTHORS:
Rocco de Filippis, Abdullah Al Foysal
KEYWORDS:
Delirium Risk Management, AI in Critical Care, ICU Workflow Optimization, Explainable AI (SHAP), Longitudinal Risk Prediction, Proactive Healthcare Interventions
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.1,
January
23,
2025
ABSTRACT: Delirium is a prevalent and severe condition in ICU patients, characterized by acute cognitive disturbances that can lead to extended hospital stays, increased healthcare costs, and heightened mortality rates. Managing delirium remains a challenge due to its multifactorial nature, involving interactions between vital signs, medications, and environmental stressors. To address this, we propose a comprehensive AI-driven framework designed to monitor, predict, and mitigate delirium risk in real-time while optimizing ICU workflows for better resource allocation. Our system integrates several advanced components: 1) a Transformer-based time-series model that predicts delirium risk dynamically, 2) an early warning mechanism to detect escalating risk levels, 3) a personalized risk scorecard offering real-time insights into contributing factors, 4) an ICU workflow optimization module identifying peak risk periods to allocate resources effectively, and 5) a longitudinal forecasting model for predicting risk trends over the next 7 days. Using a simulated dataset replicating ICU conditions, the system was evaluated for its ability to provide actionable insights through tailored interventions such as adjusting sedatives, improving oxygen saturation, or modifying environmental factors. Explainability is a cornerstone of the system, achieved through SHAP (SHapley Additive Explanations), which highlights the most critical risk contributors for individual patients. Visualizations, including early warning plots, risk trend comparisons, SHAP summary plots, and heatmaps, offer clinicians an intuitive understanding of patient risk profiles and the effectiveness of interventions. For instance, our findings show that reducing sedatives by 20% and improving SpO2 to >94% can decrease risk scores significantly. The results demonstrate the potential of this AI-driven system in transforming ICU delirium management. Early warning detection enables proactive care, while personalized scorecards and longitudinal predictions enhance decision-making. Additionally, workflow optimization reduces clinician workload, ensuring timely interventions for high-risk patients. This research sets a foundation for scalable AI solutions in ICUs, with future integration of real-world datasets and reinforcement learning to refine intervention strategies further. By leveraging real-time monitoring, explainable AI, and predictive analytics, the proposed system has the potential to revolutionize patient outcomes and operational efficiency in critical care environments.