Abstract:Introduction Despite extensive research, the goal of unravelling patient heterogeneity in critical care remains largely unattained. Combining clustering analysis of routinely collected high-frequency data with the identification of features driving cluster separation may constitute a step towards improving patient characterization. Methods In this study, we analysed prospectively collected data from 743 patients including co-morbidities, clinical examination, and laboratory parameters. We compared four cluster… Show more
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