Multifaceted visual analytics for healthcare applications


A vast amount of electronic healthcare info is now obtainable, ranging from online healthcare articles to patient electronic health records. These electronic information sets contain valuable data which will guide the decisions of each clinical professionals and patients. However, the info are typically difficult to research, in half because they usually contain multiple aspects of information. To Illustrate, patient records have information on demographics, diagnoses, medications, lab results, and symptoms. To deal with this challenge, we tend to are exploring interactive visual analysis techniques that help visualize such healthcare information in an intuitive manner and enable the invention of actionable insights. During this paper, we tend to present a review of three totally different techniques. First, we tend to describe a visual analytic system named FacetAtlas that helps users navigate a large set of disease-related documents and understand multidimensional relationships for key semantic concepts like symptoms and coverings. We tend to then present SolarMap, an alternate technique to FacetAtlas that adds visual representations of facet keyword clusters to expose greater data concerning semantic relationships. Finally, we have a tendency to describe the DICON (Dynamic Icon) visualization tool, that allows users to interactively read and refine similar multidimensional patient clusters.

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