[Paper] Risk Stratification for ICU Delirium using Pervasive Ambient Sensing Information

Source: arXiv - 2606.19292v1

Overview

Delirium is a common and serious complication in the Intensive Care Unit (ICU), associated with increased morbidity, prolonged hospital stays, and higher healthcare costs. Despite its prevalence, early prediction and prevention remain challenging. Environmental factors such as ambient sound and light may influence the onset of delirium, yet they are often overlooked in risk assessments. In this study, we examined whether light intensity and sound pressure levels can independently predict delirium across multiple prediction horizons. We evaluated four efficient sequential neural network models on data collected from 9 ICUs across 309 patients to predict delirium for 10 prediction-window sizes. We reported feature importance and direction of influence using Shapley Additive Explanations analysis. The convolutional model achieved the strongest discrimination, with AUC = 0.80 on sound data and on combined data. Sound features were the dominant predictors overall. Integrating sound with light improved short-term ($<1$ week) prediction, with the combined model assigning the highest risk immediately after the sensing period. These findings suggest that passive ambient sensing, especially sound, can add a clinically meaningful, interpretable signal for delirium risk estimation and offer a practical pathway to enrich multimodal ICU prediction and prevention strategies.

Key Contributions

This paper presents research in the following areas:

• cs.LG

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.LG.

Authors

• Jiaqing Zhang
• Sabyasachi Bandyopadhyay
• Miguel Contreras
• Jessica Sena
• Yuanfang Ren
• Andrea Davidson
• Ziyuan Guan
• Tezcan Ozrazgat-Baslanti
• Subhash Nerella
• Azra Bihorac
• Parisa Rashidi

Paper Information

• arXiv ID: 2606.19292v1
• Categories: cs.LG
• Published: June 17, 2026
• PDF: Download PDF