Interactive Visual Analysis of Heterogeneous Cohort Study Data
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.
P. Angelelli, S. Oeltze, C. Turkay, J. Haasz, E. Hodneland, A. Lundervold, A. J. Lundervold, B. Preim, and H. Hauser, "Interactive Visual Analysis of Heterogeneous Cohort Study Data," Computer Graphics and Applications, IEEE, vol. PP, iss. 99, pp. 1-1, 2014. doi:10.1109/MCG.2014.40
[BibTeX]
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.
@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",
journal = "Computer Graphics and Applications, IEEE",
year = "2014",
volume = "PP",
number = "99",
pages = "1-1",
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.",
vid = "vids/angelelli14CohortExplorer.wmv",
images = "images/angelelli14Cohort.png",
thumbnails = "images/angelelli14Cohort.png",
doi = "10.1109/MCG.2014.40",
url = "//dx.doi.org/10.1109/MCG.2014.40"
}