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Abstract

Cohort studies in medicine are conducted 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 handles partially overlapping data subsets during the interactive visualization. This model enables seamless integration of the heterogeneous data, as well as linking 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. We present case-study analyses of selected aspects of brain connectivity by using the prototype implementation of the presented model, to demonstrate its potential and flexibility.

Publication Type:	Article
Additional Information:	(c) 2014 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.
Publisher Keywords:	heterogeneous data, medical visualization, IVA
Subjects:	Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments:	School of Science & Technology > Computer Science School of Science & Technology > Computer Science > giCentre
SWORD Depositor:	Symplectic Administrator

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