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Abstract

Method and system subject matter deals with predicting delirium in patients. As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and resulting in higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool. EEG is the criterion standard but is resource intensive thus not feasible for widespread delirium monitoring. This disclosure uses limited-lead rapid-response EEG and supervised deep learning methods with vision transformer to predict delirium. Vision transformer with rapid-response EEG is capable of predicting delirium. Such monitoring is feasible in critically ill older adults, for improving delirium detection accuracy, and providing greater opportunity for individualized interventions. Benefits may shorten hospital length of stay, increase discharge to home, decrease mortality, and reduce the financial burden associated with delirium.

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