Supervised Deep Learning With Vision Transformer Predicts Delirium Using Limited Lead Eeg

This study evaluated the use of limited-lead rapid-response EEG and supervised deep learning methods with vision transformer to predict delirium in critically ill older adults. The vision transformer models provided 99.9%+ training and 97% testing accuracy across models, indicating that this method has strong potential for improving the accuracy of delirium detection and providing greater opportunity for individualized interventions.