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Interactive Visual Analysis of Heterogeneous Cohort Study Data

Paolo Angelelli, Steffen Oeltze, Cagatay Turkay, Judit Haasz, Erlend Hodneland , Arvid Lundervold, Astri Johansen Lundervold, Bernhard Preim, Helwig Hauser

ARTICLE, Computer Graphics and Applications, IEEE, 2014

Abstract

Cohort studies are used in medicine to enable the study of medical hypotheses in large samples. Often, a large amount of heterogeneous data is acquired from many subjects. The analysis is usually hypothesis-driven, i.e., a specific subset of such data is studied to confirm or reject specific hypotheses. In this paper, we demonstrate how we enable the interactive visual exploration and analysis of such data, helping with the generation of new hypotheses and contributing to the process of validating them. We propose a data-cube based model which allows to handle partially overlapping data subsets during the interactive visualization. This model enables the seamless integration of the heterogeneous data, as well as the linking of spatial and non-spatial views on these data. We implemented this model in an application prototype, and used it to analyze data acquired in the context of a cohort study on cognitive aging. In this paper we present a case-study analysis of selected aspects of brain connectivity by using a prototype implementation of the presented model, to demonstrate its potential and flexibility.

Published

Computer Graphics and Applications, IEEE

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BibTeX

@article{Angelelli14Interactive,
author={Paolo Angelelli and Steffen Oeltze and Cagatay Turkay and Judit Haasz and Erlend Hodneland
 and Arvid Lundervold and Astri Johansen Lundervold and Bernhard Preim and Helwig Hauser},
title={Interactive Visual Analysis of Heterogeneous Cohort Study Data},
year={2014},
abstract={Cohort studies are used in medicine to enable the study of medical hypotheses in large 
 samples. Often, a large amount of heterogeneous data is acquired from many subjects. The analysis 
 is usually hypothesis-driven, i.e., a specific subset of such data is studied to confirm or reject 
 specific hypotheses. In this paper, we demonstrate how we enable the interactive visual exploration 
 and analysis of such data, helping with the generation of new hypotheses and contributing to the 
 process of validating them. We propose a data-cube based model which allows to handle partially 
 overlapping data subsets during the interactive visualization. This model enables the seamless 
 integration of the heterogeneous data, as well as the linking of spatial and non-spatial views on 
 these data. We implemented this model in an application prototype, and used it to analyze data 
 acquired in the context of a cohort study on cognitive aging. In this paper we present a case-study 
 analysis of selected aspects of brain connectivity by using a prototype implementation of 
 the presented model, to demonstrate its potential and flexibility.},
journal={Computer Graphics and Applications, IEEE},
volume={PP},
number={99},
pages={1-1},
doi={10.1109/MCG.2014.40},

url = {http://dx.doi.org/10.1109/MCG.2014.40},
}






 Last Modified: Jean-Paul Balabanian, 2014-12-15