Early prediction of circulatory failure in the intensive care unit using machine learning

An early-warning system that integrates measurements from multiple organ systems using a high-resolution database with 240 patient-years of data that predicts 90% of circulatory-failure events and provides early identification of patients at risk for circulatory failure with a much lower false-alarm rate than conventional threshold-based systems.