Machine-learning improves the prediction of stroke recovery

"The key is to find the optimal neuro-rehabilitative strategy to maximize individual treatment outcome," says Professor Friedhelm Hummel, a neuroscientist and Director of the Defitech Chair for Clinical Neuroengineering at EPFL's School of Life Sciences. "If we want to address these challenges in everyday clinical practice, we have to first enhance our ability to predict the individual courses of recovery," adds Dr Philipp J. Koch, the study's first author. Hummel has now led an international team of scientists into a new approach for outcome prediction that can significantly improve stroke treatment. Publishing in the journal Brain, they demonstrate a predictive method based on two powerful, cutting-edge tools: connectomes and machine learning. The team included scientists from Sungkyunkwan University School of Medicine (Professor Y.-H.