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dc.contributor.authorBernold, Georgen_US
dc.contributor.authorMatkovic, Kresimiren_US
dc.contributor.authorGröller, Eduarden_US
dc.contributor.authorRaidou, Renata Georgiaen_US
dc.contributor.editorKozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgiaen_US
dc.date.accessioned2019-09-03T13:49:07Z
dc.date.available2019-09-03T13:49:07Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-081-9
dc.identifier.issn2070-5786
dc.identifier.urihttps://doi.org/10.2312/vcbm.20191234
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20191234
dc.description.abstractThis design study paper describes preha, a novel visual analytics application in the field of in-patient rehabilitation. We conducted extensive interviews with the intended users, i.e., engineers and clinical rehabilitation experts, to determine specific requirements of their analytical process.We identified nine tasks, for which suitable solutions have been designed and developed in the flexible environment of kibana. Our application is used to analyze existing rehabilitation data from a large cohort of 46,000 patients, and it is the first integrated solution of its kind. It incorporates functionalities for data preprocessing (profiling, wrangling and cleansing), storage, visualization, and predictive analysis on the basis of retrospective outcomes. A positive feedback from the first evaluation with domain experts indicates the usefulness of the newly proposed approach and represents a solid foundation for the introduction of visual analytics to the rehabilitation domain.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing → Visual analytics
dc.subjectApplied computing → Life and medical sciences
dc.titlepreha: Establishing Precision Rehabilitation with Visual Analyticsen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.description.sectionheadersVisual Analytics in Medicine and Biology
dc.identifier.doi10.2312/vcbm.20191234
dc.identifier.pages79-89


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