TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials.

TitleTrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials.
Publication TypeJournal Article
Year of Publication2026
AuthorsSheng R, Wang X, Wang J, Jin X, Sheng Z, Xu Z, Rajendran S, Qu H, Wang F
JournalIEEE Trans Vis Comput Graph
Volume32
Issue1
Pagination1230-1240
Date Published2026 Jan
ISSN1941-0506
KeywordsClinical Trials as Topic, Computer Graphics, Electronic Health Records, Eligibility Determination, Humans, Patient Selection
Abstract

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

DOI10.1109/TVCG.2025.3634803
Alternate JournalIEEE Trans Vis Comput Graph
PubMed ID41264440
Division: 
Institute of Artificial Intelligence for Digital Health
Category: 
Faculty Publication