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Epidemiological data comprise a plethora of sociodemographic, medical and lifestyle information gathered from questionnaires, medical examinations and imaging, usually conducted in large-scale cohort studies. Advances in data acquisition and imaging allow for generating continuously increasing amounts of large and complex datasets. As a result, following the traditional hypothesis-driven workflow of epidemiologists to assess correlations and interactions between one or multiple risk factors and the investigated outcome becomes tedious and time-consuming.
Visual Analytics can improve the understanding of high-dimensional, multi-variate, and heterogeneous cohort study data by combining data analysis techniques with visual exploration and interaction, and thus helps to generate new hypotheses.  It aims at guiding the epidemiologist to interesting subspaces and subpopulations by incorporating her expert knowledge and providing interactive filtering mechanisms to extract previously hidden patterns and to derive new insights from the data.