Abstract: Delirium is generally known to be reversible but can in fact lead to permanent cognitive dysfunction. Despite this, the early detection and reporting rate of delirium in clinical practice is less than 30%. Although there are multiple tools for the early diagnosis of delirium, they require advanced training and are not widely used, which limits their use in real-world practice. This study aimed to use machine learning to classify delirium patients to assess the ease and accuracy of this technique in clinical practice.
Keywords: Artificial Intelligence, Data Processing, Sensor, Mobile Health, Delirium.
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