Studies from Sapienza University of Rome Have Provided New Information about Biomarkers (Eeg-derived Markers To Improve Prognostic Evaluation of Disorders of Consciousness).

Předmět:	MACHINE learning PERSISTENT vegetative state TECHNOLOGICAL innovations MEDICAL informatics CONSCIOUSNESS disorders
Zdroj:	Health & Medicine Week; 12/20/2024, p6858-6858, 1p
Abstrakt:	A study conducted at Sapienza University of Rome focused on improving the prognostic evaluation of Disorders of Consciousness (DoC) through the use of EEG-derived markers. The research aimed to identify measures from routine electroencephalography (EEG) that could predict the outcome of DoC patients. By utilizing machine learning models based on EEG features, the study achieved up to 85% accuracy in outcome prediction and 76% accuracy in distinguishing between Unresponsive Wakefulness State (UWS) and Minimally Conscious State (MCS) in DoC patients. The findings suggest the potential of using routine EEG to enhance the clinical management of non-communicative patients, with further research needed to validate these results in a larger population. [Extracted from the article]
Databáze:	Complementary Index
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