Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design

摘要

ObjectiveOptimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned.