这篇论文发现ICU里的环境声音比光照更能预测谵妄，卷积模型AUC达到0.80，为无创预警提供了新思路。
该研究探讨环境声音和光照强度能否独立预测ICU谵妄。基于9个ICU的309名患者数据，评估了四种高效序贯神经网络模型在10个预测窗口上的表现。卷积模型在声音数据上取得最强辨别能力，AUC达0.80。结合声音与光照可改善短期（<1周）预测，模型在感知期结束后立即分配最高风险。
Risk Stratification for ICU Delirium using Pervasive Ambient Sensing Information
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.