New data-driven machine learning method effectively flags risk for post-stroke dangers
(University of Pennsylvania School of Medicine) A team of experts in neurocritical care, engineering, and informatics, with the Perelman School of Medicine at the University of Pennsylvania, have devised a new way to detect which stroke patients may be at risk of a serious adverse event following a ruptured brain aneurysm. This new, data-driven machine learning model, involves an algorithm for computers to combine results from various noninvasive tests to predict a secondary event.
Source: EurekAlert! - Medicine and Health - Category: Global & Universal Source Type: news
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