This research project employs machine learning algorithms integrated with computer vision, image processing, and pattern recognition technologies to perform digital analysis of facial expression behaviors in neurocritical care patients with delirium. By constructing multidimensional high-level features of delirium, the investigators have established a classification model based on behavioral. The primary objective of this study is to address the critical challenge of achieving precise and efficient delirium diagnosis in neurologically critically ill patients through automated facial expression behavior recognition.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Accuracy of the delirium prediction model
Timeframe: Through study completion, an average of 1 year
Sensitivity of the delirium prediction model
Timeframe: Through study completion, an average of 1 year
Specificity of the delirium prediction model
Timeframe: Through study completion, an average of 1 year