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2019 Computing in Cardiology Conference (CinC)
Motivation: Accounting for complex clinical dynamics in sepsis patients while aiming at an automated analysis of hourly (non-)validated data is challenging. The algorithm has to deal with imprecise, incorrect and incomplete data in addition to being time aware. Methods: We aimed to build time-specific stacked ensembles and a non-specific XGBoost learner to predict sepsis 6 hours prior to the sepsis onset. The models were trained on a triple split of 40,336 ICU stays taken from the training setsdoi:10.22489/cinc.2019.029 fatcat:2f7pnigqzfecjhu7pwbvlwwoum