Visual Analysis for Hospital Infection Control using a RNN Model

dc.contributor.authorMüller, Martinen_US
dc.contributor.authorPetzold, Markusen_US
dc.contributor.authorWunderlich, Marcelen_US
dc.contributor.authorBaumgartl, Tomen_US
dc.contributor.authorHöhn, Markusen_US
dc.contributor.authorEichel, Vanessaen_US
dc.contributor.authorMutters, Nico T.en_US
dc.contributor.authorScheithauer, Simoneen_US
dc.contributor.authorMarschollek, Michaelen_US
dc.contributor.authorLandesberger, Tatiana vonen_US
dc.contributor.editorTurkay, Cagatay and Vrotsou, Katerinaen_US
dc.date.accessioned2020-05-24T13:31:32Z
dc.date.available2020-05-24T13:31:32Z
dc.date.issued2020
dc.description.abstractBacteria and viruses are transmitted among patients in the hospital. Infection control experts develop strategies for infection control. Currently, this is done mostly manually, which is time-consuming and error-prone. Visual analysis approaches mainly focus disease spread on population level.We learn a RNN model for detection of potential infections, transmissions and infection factors. We present a novel interactive visual interface to explore the model results. Together with infection control experts, we apply our approach to real hospital data. The experts could identify factors for infections and derive infection control measures.en_US
dc.description.sectionheadersIntersecting Humans and AI
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20201090
dc.identifier.isbn978-3-03868-116-8
dc.identifier.issn2664-4487
dc.identifier.pages73-77
dc.identifier.urihttps://doi.org/10.2312/eurova.20201090
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20201090
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.titleVisual Analysis for Hospital Infection Control using a RNN Modelen_US
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