Crowdsourced Seizure Prediction

Epilepsy, which is often compared to an electrical storm in the brain, affects nearly one percent of people worldwide. The most common treatment is medication, which can leave people feeling tired or dizzy. Other options include surgery and a new type of implanted device that uses electrical pulses to prevent seizures. A better prediction algorithm has the potential to make implanted devices more effective. Ideally, the devices would work a bit like a heart defibrillator — only delivering electrical current when it's needed. However, to date  prediction algorithms have been little better than random chance. That is until the result of a competition, announced at the American Epilepsy Society's annual meeting in Seattle, showed the value of sharing a complex problem in neuroscience with experts from unrelated fields. The winning team included a mathematician and an engineer, but no doctor. The contest, with a first prize of $15,000, was sponsored by NINDS, the American Epilepsy Society and the Epilepsy Foundation. Over 500 teams entered via Kaggle.com, a website that allows researchers and companies to post data in the cloud for competitors to analyze, an approach known as crowdsourcing.
Thanks to my colleague, Mark Wilson, for finding this story in Discovermagazine.com.