Abstract
Understanding the complex relationships between a range of disparate types of data including (but not limited to) clinical signs and symptoms, socio-economic statuses, and environmental exposures is an ongoing struggle for researchers, administrators, clinicians, public health experts, and patients who struggle to use data to understand mental health. Information visualization techniques combining rich displays of data with highly responsive user interactions allow for dynamic exploration and interpretation of data to gain otherwise unavailable insights into these challenging datasets. To encourage broader adoption of visualization techniques in mental health, we draw upon research conducted over the past thirty years to introduce the reader to the field of interactive visualizations. We introduce theoretical models underlying information visualization and key considerations in the design of visualizations, including understanding user needs, managing data, effectively displaying information, and selecting appropriate approaches for interacting with the data. We introduce various types of mental health data, including survey data, administrative data, environmental data, and mobile health data, with a focus on focus on data integration and the use of predictive models. We introduce currently available open-source and commercial tools for visualization. Finally, we discuss two outstanding challenges in the field: uncertainty visualization and evaluation of visualization.
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Hochheiser, H., Verma, A. (2021). Information Visualization in Mental Health Research and Practice. In: Tenenbaum, J.D., Ranallo, P.A. (eds) Mental Health Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-70558-9_14
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