However, we found that the layouts have been used with inconsistent terms and confusion, and the lessons from previous studies are fragmented. For the last decade, these layouts have served as fundamental idioms in designing many visualization systems. We present a systematic review on three comparative layouts–juxtaposition, superposition, and explicit-encoding–which are information visualization (InfoVis) layouts designed to support comparison tasks. The platform was presented to four experts in drug safety, and is publicly available online, with the ontology of pain treatment ADE. We illustrate the interest of this platform through several use cases and we were able to find back conclusions that are known in the literature. It also relies on a 26-dimensional flower glyph for the visualization of the Adverse Drug Events (ADE) rates in 13 categories and 2 levels of seriousness. This platform is based on an ontological model including 582 trials on pain treatments, and uses semantic web technologies for querying this dataset at various levels of granularity. In this paper, we present a platform for analyzing the safety events reported during clinical trials and published in trial registries. Nevertheless, data science methods have not yet been applied widely to trial data. Since 12 years, many trial results are publicly available online in trial registries. On the contrary, in many other domains, including medical risk analysis, the advent of data science, big data and visual analytics allowed moving from expert-based to fact-based knowledge. Moreover, the independence of experts may be difficult to appraise. However, reviewing these results is a long and tedious task, hence the meta-analyses and guidelines are not updated each time a new trial is published. Trial results are reviewed by experts and consensus panels for producing meta-analyses and clinical practice guidelines. We conclude that the best options are the horizontal stacked bar chart, which allows labels to help identify the categories of adverse drug events, and the area-proportional flower glyph, which permits a better visualization of small values when very high values are present.Ĭlinical trials are the basis of Evidence-Based Medicine. We compare the four approaches, analytically and through a preliminary user study aimed at determining the user preferred visual analytics, and we identify their advantages and disadvantages. We designed four interactive visual analytics: horizontal stacked bar chart with labels, vertical stacked bar chart, area-proportional flower glyphs and star glyphs. In this paper, we focus on the visualization of adverse drug event rates, aggregated in 13 anatomical categories and 2 levels of seriousness. However, this data remains little used currently, due to the volume and the tabular presentation. Many trial results are available online in trial registries, such as. The platform was presented to four experts in drug safety, and is publicly available online, with the ontology of pain treatment ADE.ĭrugs are evaluated and compared during clinical trials, and the observed adverse drug events are recorded. We illustrate the interest of this platform through several use cases and we were able to find back conclusions that were initially found during meta-analyses. Clinical trials are the basis of Evidence-Based Medicine.
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