The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S) is an automated, physiologic measure of delirium severity that predicts clinical outcomes with a level of performance comparable to conventional interview-based clinical assessment.

We developed the E-CAM-S based on a learning-to-rank machine learning model of forehead electroencephalography signals.

Clinical delirium severity was assessed using the Confusion Assessment Method Severity (CAM-S).

We compared associations of E-CAM-S and CAM-S with hospital length of stay and inhospital mortality. E-CAM-S correlated with clinical CAM-S.

For the overall cohort, E-CAM-S and CAM-S were similar in their strength of association with hospital length of stay and inhospital mortality.

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