  Existing Intensive Care Unit (ICU) delirium prediction models do not consider
environmental factors despite strong evidence of their influence on delirium.
This study reports the first deep-learning based delirium prediction model for
ICU patients using only ambient noise and light information. Ambient light and
noise intensities were measured from ICU rooms of 102 patients from May 2021 to
September 2022 using Thunderboard, ActiGraph sensors and an iPod with
AudioTools application. These measurements were divided into daytime (0700 to
1859) and nighttime (1900 to 0659). Deep learning models were trained using
this data to predict the incidence of delirium during ICU stay or within 4 days
of discharge. Finally, outcome scores were analyzed to evaluate the importance
and directionality of every feature. Daytime noise levels were significantly
higher than nighttime noise levels. When using only noise features or a
combination of noise and light features 1-D convolutional neural networks (CNN)
achieved the strongest performance: AUC=0.77, 0.74; Sensitivity=0.60, 0.56;
Specificity=0.74, 0.74; Precision=0.46, 0.40 respectively. Using only light
features, Long Short-Term Memory (LSTM) networks performed best: AUC=0.80,
Sensitivity=0.60, Specificity=0.77, Precision=0.37. Maximum nighttime and
minimum daytime noise levels were the strongest positive and negative
predictors of delirium respectively. Nighttime light level was a stronger
predictor of delirium than daytime light level. Total influence of light
features outweighed that of noise features on the second and fourth day of ICU
stay. This study shows that ambient light and noise intensities are strong
predictors of long-term delirium incidence in the ICU. It reveals that daytime
and nighttime environmental factors might influence delirium differently and
that the importance of light and noise levels vary over the course of an ICU
stay.
