Edilberto Amorim
Massachusetts General Hospital
Predicting who will recover from coma after cardiac arrest is a major medical, economic, and ethical challenge. We applied machine learning methods to long-term electroencephalography recordings and identified indices predictive of neurological recovery from coma; further work may enable these to be used to individualize outcome predictions and guide supportive care in real-time.