Visual Analytics of Multivariate Intensive Care Time Series Data

We present an approach for visual analysis of high-dimensional measurement data with varying sampling rates as routinely recorded in intensive care units. In intensive care, most assessments not only depend on one single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate data remains a challenging task. We present a linked-view post hoc visual analytics application that reduces data complexity by combining projection-based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also of ensembles by adapting existing techniques using non-parametric statistics. We evaluated the effectiveness and acceptance of our approach through expert feedback with domain scientists from the surgical department using real-world data: a post-surgery study performed on a porcine surrogate model to identify parameters suitable for diagnosing and prognosticating the volume state, and clinical data from a public database. The results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition.

Automated summary

We propose an approach for analyzing high-dimensional measurement data in intensive care units, which have varying sampling rates. Our approach combines projection-based time curves and small multiples to reduce data complexity and supports analysis of both individual patients and ensembles. We received positive feedback from domain scientists in the surgical department through evaluation with real-world data.