Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental
problem in many areas. Pure Machine Learning (ML) approaches often fail in the small …

Introduction Determining the optimal timing for extubation can be challenging in the
intensive care. In this study, we aim to identify predictors for extubation failure in critically ill …

Abstract Background The Coronavirus disease 2019 (COVID-19) pandemic has underlined
the urgent need for reliable, multicenter, and full-admission intensive care data to advance …

OBJECTIVES: To investigate healthcare system–driven variation in general characteristics,
interventions, and outcomes in coronavirus disease 2019 (COVID-19) patients admitted to …

Background In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we
have previously developed and validated a machine learning concept in 1,677 …

Purpose To assess, validate and compare the predictive performance of models for in-
hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two …

Large and complex data sets are increasingly available for research in critical care. To
analyze these data, researchers use techniques commonly referred to as statistical learning …

Serial delay structures are of natural and practical interest, as in production, communications
and health care. Flexible service allocation might well be beneficial, particularly to handle …

Background The identification of risk factors for adverse outcomes and prolonged intensive
care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining …

Introduction Hospitals generate large amounts of data and this data is generally modeled
and labeled in a proprietary way, hampering its exchange and integration. Manually …