A multidisciplinary team of researchers in neurocritical care, engineering, and informatics 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 non-invasive tests to predict a secondary event. Preliminary results were released at the recent Neurocritical Care Society Annual Meeting in Philadelphia.
Comparing 89 patient cases retrospectively, the team found that automated features of existing ICU data were as effective as the transcranial Doppler procedure currently used to detect a dangerous constriction of blood vessels in the brain.
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