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ACGM: Attribute-Centric Graph Modeling Network for Concurrent Missing Tabular Data Imputation and COVID-19 Prognosis.

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    This study introduces ACGM, a novel network for COVID-19 prognosis using clinical data. It effectively handles missing values and imbalanced data, improving prediction accuracy.

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    Area of Science:

    • Medical Informatics
    • Machine Learning
    • Computational Biology

    Background:

    • COVID-19 prognosis using clinical data is challenging due to missing values and imbalanced datasets.
    • Existing methods fail to capture complex inter-attribute relationships and lack training stability.

    Purpose of the Study:

    • To propose ACGM (Attribute-Centric Graph Modeling network) for simultaneous missing data imputation and COVID-19 prognosis.
    • To address limitations of existing methods in handling data complexity and instability.

    Main Methods:

    • ACGM utilizes three modules: Attributes Preprocessing Module (APM), Graph-Enhanced Attributes Imputation Module (GEAIM), and Graph-Enhanced Disease Prognosis Module (GEDPM).
    • GEAIM and GEDPM employ a mean-teacher strategy with graph matching to model high-order attribute relationships, enhance stability, and preserve structural integrity.

    Main Results:

    • ACGM demonstrated superior performance over existing methods on four public COVID-19 datasets.
    • Interpretability analysis identified LDH, Difficulty In Breathing, and SaO2 as key prognostic factors.

    Conclusions:

    • ACGM effectively handles missing data and class imbalance for improved COVID-19 prognosis.
    • The model's findings align with clinical insights, highlighting its potential for real-world application.