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    Visual Analysis of Multivariate Intensive Care Surveillance Data

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    Date
    2020
    Author
    Brich, Nicolas ORCID
    Schulz, Christoph ORCID
    Peter, Jörg ORCID
    Klingert, Wilfried ORCID
    Schenk, Martin ORCID
    Weiskopf, Daniel ORCID
    Krone, Michael ORCID
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    Abstract
    We 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.
    BibTeX
    @inproceedings {10.2312:vcbm.20201174,
    booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
    editor = {Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia},
    title = {{Visual Analysis of Multivariate Intensive Care Surveillance Data}},
    author = {Brich, Nicolas and Schulz, Christoph and Peter, Jörg and Klingert, Wilfried and Schenk, Martin and Weiskopf, Daniel and Krone, Michael},
    year = {2020},
    publisher = {The Eurographics Association},
    ISSN = {2070-5786},
    ISBN = {978-3-03868-109-0},
    DOI = {10.2312/vcbm.20201174}
    }
    URI
    https://doi.org/10.2312/vcbm.20201174
    https://diglib.eg.org:443/handle/10.2312/vcbm20201174
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA