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FA Francesca Alfieri
AA Andrea Ancona
GT Giovanni Tripepi
DC Dario Crosetto
VR Vincenzo Randazzo
AP Annunziata Paviglianiti
EP Eros Pasero
LV Luigi Vecchi
VC Valentina Cauda
RF Riccardo Maria Fagugli
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A feature is a peculiar attribute extracted from raw data. Features used in the current study were extracted from the hourly urine output trend of the patients and used for the logistic regression analysis. They are 11 values calculated as the minimum average values obtained by passing a series of sliding windows with a variable size in the range [2, 12] over the hourly urine output data from ICU admission to the hour of prediction (Fig. 2).

Example of extraction of the feature corresponding to a window size equal to 12. The process is repeated for all window sizes in range (2-12), thus obtaining 11 features

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