Visual Analysis of Cohort Study Datasets
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M. D. Steenwijk, J. Milles, M. A. Buchem, J. H. Reiber, and C. P. Botha, Integrated Visual Analysis for Heterogeneous Datasets in Cohort Studies, in IEEE VisWeek Workshop on Visual Analytics in Health Care, 2010. |
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Abstract
Current medical research is often hypothesis driven, focusing on a limited number of parameters showing, or expected to show, some relation with the disease. When a supporting scientific ground or proper hypothesis is lacking however, this approach is not always fruitful. Visual analytics has seen limited application in medical research. We propose that visual analytics can be used to study parameters across patients, especially in cases where no clear hypothesis is available from the start. This can help medical researchers to focus their efforts. We present a visual analysis framework which provides highly interactive visual analysis of cohort data, and is able to deal with irregular multi-timepoint, imaging and non-imaging data. The framework integrates the extraction of features into the process of visual analysis and makes use of a carefully designed data structure able to keep track of data dependencies and interrelationships in inhomogeneous cohort study data. We evaluated the framework on a cohort of patients suspected of having neuropsychiatric SLE, a heterogeneous rheumatic disease. Visual analysis revealed a number of observations corroborating earlier findings. We were also able to identify new trends in the data that could indicate directions for further research, and illustrated thereby the potential of visual analytics to operate as a hypothesis generating tool.