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Intelligent Data-Driven Model for Diabetes Diurnal Patterns Analysis.

Mohammad R Eissa, Tim Good, Jackie Elliott

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    Accurate insulin dosing for type 1 diabetes requires precise identification of daily routines. This study introduces a data-driven model to automatically detect diurnal patterns, improving insulin sensitivity factor and carbohydrate ratio settings for better diabetes management.

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    Area of Science:

    • Endocrinology and Metabolism
    • Biomedical Data Science
    • Artificial Intelligence in Healthcare

    Background:

    • Type 1 diabetes management relies on accurate insulin dosing, influenced by diurnal activity patterns.
    • Current bolus advisors use fixed insulin-to-carbohydrate ratios and insulin sensitivity factors, often based on inaccurate self-reported routines.
    • Static default settings in diabetes devices fail to adapt to changing daily routines, potentially leading to suboptimal glycemic control.

    Purpose of the Study:

    • To develop a data-driven model for accurate identification of diurnal patterns in type 1 diabetes.
    • To improve the personalization and accuracy of insulin dose calculations by optimizing time-dependent parameters in bolus advisors.
    • To address the limitations of self-reporting and default settings in current diabetes management technology.

    Main Methods:

    • Utilized self-monitoring data from individuals with type 1 diabetes.
    • Employed time-series clustering to identify distinct diurnal activity patterns.
    • Developed a model to automatically detect daily time periods and suggest necessary adjustments to bolus calculator settings.

    Main Results:

    • The proposed model successfully identifies diurnal patterns and daily time periods from self-monitoring data.
    • Demonstrated the potential for a more granular, accurate, and personalized daily time setting profile for bolus advisors.
    • Identified opportunities for enhanced bolus advisor settings, including week/weekend variations and modifiable daily time configurations.

    Conclusions:

    • A data-driven approach using time-series clustering can effectively identify diabetes diurnal patterns.
    • This method offers a more accurate and personalized way to set time-dependent parameters in bolus advisors, improving self-management.
    • The model provides valuable contextual insights into glycemic patterns for both patients and clinicians, enhancing diabetes care.