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Visual Exploration and Cohort Identification of Acute Patient Histories Aggregated from Heterogeneous Sources

Sætre, Rune; Nytrø, Øystein; Nordbø, Stein Jakob; Steinsbekk, Aslak
Chapter, Conference object, Peer reviewed
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URI
http://hdl.handle.net/11250/2393508
Date
2016
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  • Institutt for datateknologi og informatikk [3877]
  • Institutt for samfunnsmedisin og sykepleie [1769]
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Original version
IEEE, ICDE [Eds.] 2016 IEEE 32nd International Conference on Data Engineering Workshops p. 71-77 Proceedings / International Conference on Data Engineering, IEEE Computer Society, 2016  
Abstract
How 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.
Publisher
IEEE

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