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Personalized Model Identification for Glucose Dynamics From Clinical Data With Incomplete Inputs.

Basak Ozaslan, Eleonora M Aiello, Emilia Fushimi

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    This study introduces a new method to improve metabolic models for type 1 diabetes by reconstructing corrupted clinical data. This approach enhances glucose control predictions, aiding treatment decisions.

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    Area of Science:

    • Biomedical Engineering
    • Computational Biology
    • Data Science

    Background:

    • Clinical data for metabolic modeling often contains incomplete or imprecise information.
    • Accurate metabolic models are crucial for managing type 1 diabetes (T1D).

    Purpose of the Study:

    • To develop a method for reconstructing corrupted input data in clinical datasets.
    • To jointly identify person-specific parameters of a metabolic model for T1D.

    Main Methods:

    • An iterative algorithm combining nonlinear least-squares and mixed-integer quadratic programming.
    • Optimization designed for computational tractability and robustness against data inaccuracies.
    • Personalized hyperparameter tuning for individual patient data.

    Main Results:

    • The proposed method improved model prediction capabilities on unseen data compared to standard least squares.
    • An average 2.2% improvement in Mean Absolute Relative Difference (MARD) for a two-hour prediction horizon was observed (p=0.0006).

    Conclusions:

    • The developed method effectively identifies models from clinical data with unknown input inaccuracies.
    • Accurate personalized models can significantly inform treatment decisions and improve glucose control in T1D patients.