Before revision:  Our main motivation is to model the dataset mathematically and to quantify the deterioration of the model's forecast  performance relative to its past performance. Our data-sampling strategy around putative time-points of critical transitions offer early differentiation capability between septic shock and non-sepsis patients .
After revision:  Our main motivation is to model the dataset mathematically and to quantify the deterioration of the model's forecast performance relative to its past (in-sample) performance. Considering the highest value of the moving average of SL as a critical transition, our retrospective analysis revealed that critical transitions occurred at a median of over 35 hours before the onset of septic shock, which suggests the applicability of our method as an early-warning indicator. Furthermore, we show that clinical variables at critical-transition regions are significantly different between septic shock and non-sepsis patients .