IEEE Trans Vis Comput Graph. 2019 Mar;25(3):1615-1628. doi: 10.1109/TVCG.2018.2803829. Epub 2018 Feb 8.
In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.
在这项设计研究中,我们提出了一种可视化技术,该技术将患者的病史分段,而不是将其视为原始事件序列,然后使用整个病史或治疗组合等标准对段进行聚合,并将聚合的段可视化作为静态仪表板,以显示纵向变化。静态仪表板经过九次迭代开发,显示了来自患者病史的 15 个重要属性。最终的设计由五名非专家、五名可视化专家和四名医学专家进行了评估,他们成功地使用它来概览了一个包含 2000 名患者的数据集,并观察了两个队列之间的纵向变化和差异。该研究代表了使用仪表板成功可视化的大规模数据细节的重大变化,并提供了有关如何推广该方法的指导。