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2 The goal of visual analytics is to permit people to draw conclusions that lead to better decisions by visually representing information in a way that allows direct interaction with the data and can provide new insights. Visual analytics enhances the concept of information visualization and can be seen as an integrated approach combining visualization, human factors, and data analysis. As we show, it can be used to classify data by characteristics, such as in demographic, geographic, and neuroimaging classifications.
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In this article, we demonstrate exploratory analysis techniques using Microsoft Live Labs Pivot technology, 1 a visual analytics tool that offers a fresh way to visually browse and arrange massive amounts of data (and images) online. Visual analytics not only permit users to detect expected events, such as those that might be predicted by models, but also help users discover the unexpected-surprising anomalies, changes, patterns, and relationships that can then be examined and assessed to develop new insights. Visual analytics can facilitate the discourse between the user and the data by providing the opportunity for visual interaction with the data in a way that can support analytical reasoning and the exploration of data from multiple perspectives. Using a clinical research database to facilitate potential cohort discovery and recruit patients for possible future studies is not a new concept, but forming hypotheses from data sets consisting of hundreds to thousands of variables and analyzing them in an intuitive way is a very challenging and complex process. Keywords: clinical research translational research visual analytics research data warehouse medical informatics biomedical informatics Introduction The development of visual analytics using Microsoft Live Labs Pivot makes the process of data elaboration, information gathering, knowledge generation, and complex information exploration transparent to tool users and provides researchers with the ability to sort and filter by various criteria, which can lead to strong, novel hypotheses. PRD Pivot is a de-identified pediatric research database designed to make secondary use of rich data sources, such as the electronic health record (EHR).
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To overcome these issues and to assist researchers in building hypotheses from raw data, we are working on a visual and analytical platform called PRD Pivot. Despite the availability of data, generating hypotheses from huge data sets is often challenging, and the lack of complex analysis of data might lead to weak hypotheses. Secondary use of large and open data sets provides researchers with an opportunity to address high-impact questions that would otherwise be prohibitively expensive and time consuming to study. By Teeradache Viangteeravat, PhD, and Naga Satya V.