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Related Experiment Video

Updated: Oct 18, 2025

A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS
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ThreadStates: State-based Visual Analysis of Disease Progression.

Qianwen Wang, Tali Mazor, Theresa A Harbig

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    Summary

    ThreadStates visualizes longitudinal patient data to reveal disease progression states and patterns. This tool helps researchers identify patient groups and understand disease trajectories more effectively.

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    Area of Science:

    • Biomedical Informatics
    • Data Visualization
    • Computational Biology

    Background:

    • Longitudinal cohort studies generate rich patient observation data over time, crucial for understanding disease progression.
    • Existing visual analysis tools often treat these observations as general events, underutilizing their potential for disease modeling.
    • There is a need for specialized tools to effectively analyze and interpret complex longitudinal patient data.

    Purpose of the Study:

    • To introduce ThreadStates, an interactive visual analytics tool designed for exploring longitudinal patient cohort data.
    • To enable the identification and refinement of disease progression states through a human-in-the-loop approach.
    • To reveal disease progression patterns and facilitate comparisons between patient groups.

    Main Methods:

    • Development of a novel Glyph Matrix combined with a scatter plot for state identification and refinement.
    • Utilizing Sankey-based visualizations to illustrate disease progression pathways and state transitions.
    • Employing sequence clustering techniques to group patients by progression patterns and analyze associations with patient features.

    Main Results:

    • ThreadStates successfully summarizes complex disease states from longitudinal observation data.
    • The tool effectively reveals disease progression patterns and state transitions.
    • It enables meaningful comparisons between different patient groups based on their progression trajectories.

    Conclusions:

    • ThreadStates enhances the analysis of longitudinal patient data by providing interactive visualization of disease progression.
    • The tool facilitates a deeper understanding of disease trajectories and patient stratification.
    • It represents a significant advancement in visual analytics for biomedical research involving cohort studies.