A machine-learning seizure burden monitoring algorithm was analyzed in a study of 334 cases from the SAFER-EEG clinical trial. The algorithm predicted poor functional outcomes in patients with neurological conditions, with 79% of those with a seizure burden of at least 50% being discharged to a long-term care facility. The research, presented at the American Academy of Neurology meeting, highlighted the importance of automated monitoring of epileptiform activity and seizure burden. The findings suggest a potential shift towards integrating AI and machine learning in the management of critically ill patients with seizures.
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Monitoring algorithm predicts association of seizure burden with functional outcomes
