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    This study introduces a novel Variational Hawkes Process (VHP) model for predicting disease progression using patient health data. The VHP model accurately forecasts future patient states from irregular historical medical records.

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    Area of Science:

    • Computational biology
    • Health informatics
    • Machine learning for healthcare

    Background:

    • Longitudinal patient data offers insights into disease progression and healthcare delivery.
    • Modeling disease trajectories is challenging due to complexity and irregular temporal data.

    Purpose of the Study:

    • To develop a quantitative model for predicting future patient states based on historical disease trajectories.
    • To address the complexities of irregular time-series data in medical records.

    Main Methods:

    • Proposed a novel Variational Hawkes Process (VHP) model.
    • Integrated Hawkes Process for irregular visit intensity and Variational Auto-Encoder for trajectory representation.
    • Employed a disease trajectory discriminator to enhance prediction accuracy.

    Main Results:

    • The VHP model significantly outperformed existing baseline models on multiple datasets.
    • Demonstrated effectiveness on heart failure and sepsis patient data from MIMIC-III and a Chinese hospital.

    Conclusions:

    • The VHP model provides a robust approach for predictive analysis of disease progression.
    • Findings have practical implications for disease management and healthcare applications.