Computer scientists from Duke University and Harvard University have joined with physicians from Massachusetts General Hospital and the University of Wisconsin to develop a machine learning model that can predict which patients are most at risk of having destructive seizures after suffering a stroke or other brain injury.
A point system they’ve developed helps determine which patients should receive expensive continuous electroencephalography (cEEG) monitoring. Implemented nationwide, the authors say their model could help hospitals monitor nearly three times as many patients, saving many lives as well as $54 million each year.
A paper detailing the methods behind the interpretable machine learning approach appeared online June 19 in the Journal of Machine Learning Research.
When a brain aneurysm leads to a brain bleed, much of the damage isn’t done in just the first few hours, it accumulates over time as the patient experiences seizures. But because the patient’s condition doesn’t allow them to show any outward signs of distress, the only way to tell they are having seizures is through an EEG. However, continuously monitoring a patient with this technology is expensive and requires highly trained physicians to interpret the readings.
Aaron Struck, assistant professor of neurology in the University of Wisconsin School of Medicine and Public Health, and Brandon Westover, director of the Critical Care EEG Monitoring Service at Massachusetts General Hospital, sought to optimize these limited resources. Through the help of colleagues in the Critical Care EEG Monitoring Research Consortium, they collected data on dozens of variables from nearly 5,500 patients and got to work.