Visual Analysis of Multivariate Intensive Care Surveillance Data

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dc.contributor.authorBrich, Nicolasen_US
dc.contributor.authorSchulz, Christophen_US
dc.contributor.authorPeter, Jörgen_US
dc.contributor.authorKlingert, Wilfrieden_US
dc.contributor.authorSchenk, Martinen_US
dc.contributor.authorWeiskopf, Danielen_US
dc.contributor.authorKrone, Michaelen_US
dc.contributor.editorKozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgiaen_US
dc.date.accessioned2020-09-28T06:11:51Z
dc.date.available2020-09-28T06:11:51Z
dc.date.issued2020
dc.description.abstractWe present an approach for visual analysis of high-dimensional measurement data with varying sampling rates in the context of an experimental post-surgery study performed on a porcine surrogate model. The study aimed at identifying parameters suitable for diagnosing and prognosticating the volume state-a crucial and difficult task in intensive care medicine. In intensive care, most assessments not only depend on a single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate time-dependent data remains a challenging task. We present a linked-view post hoc visual analysis 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 the analysis of ensembles by adapting existing techniques using non-parametric statistics. We evaluated the effectiveness and acceptance of our application through expert feedback with domain scientists from the surgical department using real-world data: 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. Furthermore, the medical experts believe that our method can be transferred from medical research to the clinical context, for example, to identify the early onset of a sepsis.en_US
dc.description.sectionheadersVA and Uncertainty
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20201174
dc.identifier.isbn978-3-03868-109-0
dc.identifier.issn2070-5786
dc.identifier.pages71-83
dc.identifier.urihttps://doi.org/10.2312/vcbm.20201174
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20201174
dc.publisherThe Eurographics Associationen_US
dc.subjectApplied computing
dc.subjectHealth care information systems
dc.subjectMathematics of computing
dc.subjectTime series analysis
dc.subjectDimensionality reduction
dc.subjectHuman centered computing
dc.subjectInformation visualization
dc.titleVisual Analysis of Multivariate Intensive Care Surveillance Dataen_US
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VCBM 2020: Eurographics Workshop on Visual Computing for Biology and Medicine