TrialView: An AI-powered Visual Analytics System for Temporal Event Data in Clinical Trials

dc.contributor.authorLi, Zuotian
dc.contributor.authorLiu, Xiang
dc.contributor.authorCheng, Zelei
dc.contributor.authorChen, Yingjie
dc.contributor.authorTu, Wanzhu
dc.contributor.authorSu, Jing
dc.date.accessioned2023-12-26T18:36:47Z
dc.date.available2023-12-26T18:36:47Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.141
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other0875b898-b80a-487a-83a0-9e5fe869dab2
dc.identifier.urihttps://hdl.handle.net/10125/106518
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Intelligence and Visual Analytics
dc.subjectclinical trial
dc.subjectcluster model
dc.subjectgraph ai
dc.subjectvisual analytics
dc.titleTrialView: An AI-powered Visual Analytics System for Temporal Event Data in Clinical Trials
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractRandomized 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.
dcterms.extent10 pages
prism.startingpage1169

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