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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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We present an interactive visual analytics system for classification, iVisClassifier, based on a supervised dimension reduction method, linear discriminant analysis (LDA). Given high-dimensional data and associated cluster labels, LDA gives their reduced dimensional representation, which provides a good overview about the cluster structure. Instead of a single two-or three-dimensional scatter plot, iVisClassifier fully interacts with all the reduced dimensions obtained by LDA through parallel coordinates and a scatter plot. Furthermore, it significantly improves the interactivity and interpretability of LDA. LDA enables users to understand each of the reduced dimensions and how they influence the data by reconstructing the basis vector into the original data domain. By using heat maps, iVisClassifier gives an overview about the cluster relationship in terms of pairwise distances between cluster centroids both in the original space and in the reduced dimensional space. Equipped with these functionalities, iVisClassifier supports users' classification tasks in an efficient way. Using several facial image data, we show how the above analysis is performed.

Clustering plays an important role in many large

Low-dose oral corticosteroids are effective without serious side-effects in preventing the progression and inducing repigmentation of actively spreading vitiligo, which is difficult to treat with topical corticosteroids or photochemotherapy.