Randomized controlled trials (RCT) are the gold standards for evaluating the efficacy and safety of therapeutic interventions in human subjects. In addition to the pre-specified endpoints, trial participants' experience reveals the time course of the intervention. Few analytical tools exist to summarize and visualize the individual experience of trial participants. Visual analytics allows integrative examination of temporal event patterns of patient experience, thus generating insights for better care decisions. Towards this end, we introduce TrialView, an information system that combines graph artificial intelligence (AI) and visual analytics to enhance the dissemination of trial data. TrialView offers four distinct yet interconnected views: Individual, Cohort, Progression, and Statistics, enabling an interactive exploration of individual and group-level data. The TrialView system is a general-purpose analytical tool for a broad class of clinical trials. The system is powered by graph AI, knowledge-guided clustering, explanatory modeling, and graph-based agglomeration algorithms. We demonstrate the system's effectiveness in analyzing temporal event data through a case study.

翻译：随机对照试验（RCT）是评估人体干预措施疗效与安全性的金标准。除预设终点外，受试者的体验揭示了干预措施的时间进程。当前鲜有分析工具能够归纳并可视化受试者的个体体验。视觉分析技术可整合检验患者体验的时序事件模式，从而为优化诊疗决策提供洞见。为此，我们提出TrialView系统——一种融合图人工智能（AI）与视觉分析的信息系统，旨在增强试验数据的传播效能。TrialView提供四种相互关联的视图：个体视图、队列视图、进展视图与统计视图，支持对个体及群体数据开展交互式探索。该系统作为通用型分析工具，适用于广谱临床试验，其核心功能依托于图AI、知识引导聚类、解释性建模及基于图的凝聚算法。本文通过案例研究验证了该系统在分析时序事件数据方面的有效性。