Before revision:  These transitions are often phase transitions that generate early-warning signs and provide insights into the underlying disease-driving mechanism(s) . In this paper, we propose a computational method to discover indicators of such transitions in a multivariate time series dataset of critical care patients (MIMIC-III database).
After revision:  These transitions are often phase transitions that generate early-warning signs and provide insights into the underlying disease-driving mechanism(s) . In this paper, we propose a computational method based on surprise loss (SL) to discover data-driven indicators of such transitions in a multivariate time series dataset of critical care patients (MIMIC-III database).