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dc.contributor.authorSætre, Rune
dc.contributor.authorNytrø, Øystein
dc.contributor.authorNordbø, Stein Jakob
dc.contributor.authorSteinsbekk, Aslak
dc.date.accessioned2016-05-19T20:15:41Z
dc.date.accessioned2016-06-22T08:00:48Z
dc.date.available2016-05-19T20:15:41Z
dc.date.available2016-06-22T08:00:48Z
dc.date.issued2016
dc.identifier.citationIEEE, ICDE [Eds.] 2016 IEEE 32nd International Conference on Data Engineering Workshops p. 71-77 Proceedings / International Conference on Data Engineering, IEEE Computer Society, 2016nb_NO
dc.identifier.isbn978-1-5090-2109-3
dc.identifier.issn1084-4627
dc.identifier.urihttp://hdl.handle.net/11250/2393508
dc.description.abstractHow can we use information visualization to support retrospective, exploratory analysis of collections of histories for patients admitted to acute care? This paper describes a novel design for visual cohort identification and exploration. We have developed a tool that integrates multiple, heterogeneous clinical data sources and allows alignment, querying and abstraction in a common workbench. This paper presents results from two projects and a review of related work in the field of information visualization including both presentation and interactive navigation of the information. We have developed an interactive prototype and present the visualization aspect of this prototype and a brief demonstration of its use in a research project with a large cohort of patients. The prototype represents and reasons with patient events in different OWL-formalizations according to the perspective and use: One for integration and alignment of patient records and observations; Another for visual presentation of individual or cohort trajectories. Health researchers have successfully analyzed large cohorts (over 100,000 individuals) using the tool. We have also used the tool to produce interactive personal health time-lines (for more than 10,000 individuals) on the web. Utility, usability and effect have been tested extensively and the results so far are promising. We envision that clinicians who want to learn more about groups of patients and their treatment processes will find the tool valuable. In addition, we believe that the visualization can be useful to researchers looking at data to be statistically evaluated, in order to discover new hypotheses or get ideas for the best analysis strategies. Our main conclusion is that the tool is usable, but it can be challenging to use for large data sets.nb_NO
dc.language.isoengnb_NO
dc.publisherIEEEnb_NO
dc.relation.urihttps://www.ntnu.no/wiki/download/attachments/43026978/SelfArchive.pdf
dc.titleVisual Exploration and Cohort Identification of Acute Patient Histories Aggregated from Heterogeneous Sourcesnb_NO
dc.typeChapternb_NO
dc.typeConference objectnb_NO
dc.typePeer reviewednb_NO
dc.date.updated2016-05-19T20:15:40Z
dc.source.pagenumber71-77nb_NO
dc.identifier.cristin1356423
dc.relation.projectNorges forskningsråd: 213973nb_NO
dc.description.localcodePostprint version. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.nb_NO


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