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.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Predictive Performance for Delirium (Area Under the Receiver Operating Characteristic Curve, AUROC
Timeframe: 3 to 4 days (during the longitudinal EEG data collection period)