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

TL;DR

TrialView is an innovative visual analytics system that leverages AI and graph algorithms to explore and understand temporal event data in clinical trials, aiding better decision-making.

Contribution

It introduces a novel AI-powered visual analytics platform with multiple interactive views for analyzing temporal data in clinical trials, integrating graph AI and clustering techniques.

Findings

Effective analysis of temporal event data demonstrated in a case study.

Enhanced understanding of patient experiences through interactive visualizations.

System's versatility for various clinical trial types.

Abstract

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.