Web-based geotemporal visualization of healthcare data
Abstract
Healthcare data visualization presents challenges due to its non-standard organizational structure and disparate record formats. Epidemiologists and clinicians currently lack the tools to discern patterns in large-scale data that would reveal valuable healthcare information at the granular level of individual patients and populations. Integrating geospatial and temporal healthcare data within a common visual context provides a twofold benefit: it allows clinicians to synthesize large-scale healthcare data to provide a context for local patient care decisions, and it better informs epidemiologists in making public health recommendations. Advanced implementations of the Scalable Vector Graphic (SVG), HyperText Markup Language version 5 (HTML5), and Cascading Style Sheets version 3 (CSS3) specifications in the latest versions of most major Web browsers brought hardware-accelerated graphics to the Web and opened the door for more intricate and interactive visualization techniques than have previously been possible. We developed a series of new geotemporal visualization techniques under a general healthcare data visualization framework in order to provide a real-time dashboard for analysis and exploration of complex healthcare data. This visualization framework, HealthTerrain, is a concept space constructed using text and data mining techniques, extracted concepts, and attributes associated with geographical locations. HealthTerrain's association graph serves two purposes. First, it is a powerful interactive visualization of the relationships among concept terms, allowing users to explore the concept space, discover correlations, and generate novel hypotheses. Second, it functions as a user interface, allowing selection of concept terms for further visual analysis. In addition to the association graph, concept terms can be compared across time and location using several new visualization techniques. A spatial-temporal choropleth map projection embeds rich textures to generate an integrated, two-dimensional visualization. Its key feature is a new offset contour method to visualize multidimensional and time-series data associated with different geographical regions. Additionally, a ring graph reveals patterns at the fine granularity of patient occurrences using a new radial coordinate-based time-series visualization technique.
Degree
M.S.
Advisors
Fang, Purdue University.
Subject Area
Information Technology|Health care management|Computer science
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