Early Delirium Prediction Via Serial EEG Trajectories and Machine Learning

The goal of this observational study is to develop a machine learning model that can predict delirium in trauma patients before it clinically appears. The study focuses on analyzing brainwave (EEG) patterns collected over several days in the trauma ICU. By comparing different recording conditions-such as having eyes open versus closed-researchers aim to identify the most effective way to monitor brain health and detect early signs of delirium in critically ill patients.