New data-driven machine learning method effectively flags risk for post-stroke dangers

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.

Fuente : http://www.eurekalert.org/pub_releases/2013-10/uop...