Decision Intelligence and Visual Analytics

Permanent URI for this collectionhttps://hdl.handle.net/10125/107425

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    TrialView: An AI-powered Visual Analytics System for Temporal Event Data in Clinical Trials
    (2024-01-03) Li, Zuotian; Liu, Xiang; Cheng, Zelei; Chen, Yingjie; Tu, Wanzhu; Su, Jing
    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.
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    Exploring Automated Data Augmentation Approaches for Deep Learning: A Case Study of Individual Feral Cat Classification
    (2024-01-03) Yang, Zihan; Sinnott, Richard; Bailey, James; Ehinger, Krista A.
    This paper evaluates the performance of several automated data augmentation (AutoDA) methods for image classification problems suited for scenarios with limited and potentially imbalanced data sets. We compare one-stage, two-stage and search-free methods. These are explored in the context of a case study to identify/count feral cats in rural Victoria. Our results show that a trade-off exists between accuracy and efficiency, with one-stage methods being faster but less accurate than two-stage methods. Search-free methods are fastest, but have limited improvement in the resultant classification accuracy.
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    Introduction to the Minitrack on Decision Intelligence and Visual Analytics
    (2024-01-03) Ebert, David; Fisher, Brian; Gaither, Kelly