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    Visual analytics tools can introduce selection bias when creating patient cohorts from complex health data. New techniques track and visualize this bias, ensuring more representative patient groups for research.

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    Area of Science:

    • Information Visualization
    • Data Science
    • Health Informatics

    Background:

    • Large, complex datasets are prevalent across domains, necessitating advanced visual analytics tools for exploration.
    • Current visual analytics systems struggle to visualize high-dimensional data (e.g., electronic health records), potentially leading to selection bias.
    • Selection bias occurs when cohorts are formed based on a subset of dimensions, making them unrepresentative of the intended population.

    Purpose of the Study:

    • To present techniques for tracking and visualizing selection bias in high-dimensional visual analytics.
    • To address the challenge of maintaining cohort representativeness in complex data exploration, particularly in medical informatics.
    • To improve the validity of analyses conducted on user-defined cohorts.

    Main Methods:

    • Developed tree-based cohort provenance and visualization to track cohort creation and compare against a baseline.
    • Introduced visual encoding of cohort 'drift' to highlight potential selection bias.
    • Created a novel icicle-plot based visualization for detailed per-dimension comparison between baseline and focus cohorts.

    Main Results:

    • The proposed techniques effectively track and visualize selection bias in high-dimensional datasets.
    • The methods were integrated into a medical temporal event sequence visual analytics tool.
    • User interviews with domain experts validated the utility and effectiveness of the developed techniques.

    Conclusions:

    • Selection bias is a critical issue in visual analytics of complex datasets, especially in healthcare.
    • The presented techniques offer a robust solution for mitigating and understanding selection bias.
    • These advancements enhance the reliability of data analysis and research using visual analytics tools.