Fig. 1: In EventFlow, the original LABA dataset, consisting of over 2700 visual elements (left), was quickly pared down to the events most critical to the study. The simplified dataset (right) consists of only 492 visual elements, an 80% reduction in visual complexity. From this simplified figure, aligned by the patients' "new" LABA prescription, researchers were immediately able to notice the data sparsity on the left side of the alignment point, indicating that patients had not received other treatments in the months leading up to their LABA prescription (i.e. not following the recommended practices).Abstract-Electronic Health Records (EHRs) have emerged as a cost-effective data source for conducting medical research. The difficulty in using EHRs for research purposes, however, is that both patient selection and record analysis must be conducted across very large, and typically very noisy datasets. Our previous work introduced EventFlow, a visualization tool that transforms an entire dataset of temporal event records into an aggregated display, allowing researchers to analyze population-level patterns and trends. As datasets become larger and more varied, however, it becomes increasingly difficult to provide a succinct, summarizing display. This paper presents a series of user-driven data simplifications that allow researchers to pare event records down to their core elements. Furthermore, we present a novel metric for measuring visual complexity, and a language for codifying disjoint strategies into an overarching simplification framework. These simplifications were used by real-world researchers to gain new and valuable insights from initially overwhelming datasets.

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Geographical Information Systems have been increasingly used to aid the prompt detection, tracking, and analysis of disease outbreaks. Web content which is full of healthrelated data also serves as a useful resource for disease outbreak analysis. News posts often report the initial outbreak of diseases and contain valuable information that aids in ascertaining the time and location of the disease outbreak. The locations mentioned in the news posts are specified textually rather than geometrically thereby requiring the use of geotagging methods to detect them and to map the textual specification to the corresponding actual geometric specification. The NewsStand system which aggregates news posts by topic and location while providing a map query interface to them is enhanced to enable disease tracking and analysis by geotagging disease-related web news posts. Besides the powerful functionalities of NewsStand for news exploration, enhancements of NewsStand with respect to the analysis of temporal information are described which include a well-designed time slider, a heatmap-based visualization tool for displaying disease distribution, and intuitive spatiotemporal querying methods. Future improvements to NewsStand are also discussed.

The Food and Drug Administration and Department of Defense were interested in detecting sub-optimal use of long-acting beta-agonists (LABAs)

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