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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes.

Mirko Paolo Barbato, Giorgia Rigamonti, Davide Marelli

    IEEE Journal of Biomedical and Health Informatics
    |November 17, 2025
    PubMed
    Summary

    A new lightweight AI model accurately predicts blood glucose levels for Type 1 Diabetes (T1D) management. This efficient model is optimized for wearable devices, improving safety by detecting hypo- and hyperglycemic events.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Healthcare
    • Endocrinology

    Background:

    • Type 1 Diabetes (T1D) necessitates continuous glucose monitoring to prevent dangerous hypo- and hyperglycemic events.
    • Current continuous glucose monitoring (CGM) systems face challenges deploying predictive models on resource-constrained wearable devices.
    • Existing predictive models often struggle with computational and memory limitations inherent in edge devices.

    Purpose of the Study:

    • To develop a novel Lightweight Sequential Transformer model for efficient blood glucose prediction in T1D.
    • To optimize the model for deployment on resource-constrained wearable devices.
    • To address data imbalance issues common in hypo- and hyperglycemic event prediction.

    Main Methods:

    • Proposed a Lightweight Sequential Transformer architecture integrating attention mechanisms with recurrent neural network sequential processing.
    • Optimized the model for computational efficiency and deployment on edge devices.
    • Implemented a balanced loss function to manage imbalanced hypo- and hyperglycemic event data.

    Main Results:

    • The proposed model demonstrated superior performance in predicting glucose levels compared to state-of-the-art methods on the OhioT1DM and DiaTrend datasets.
    • The model successfully detected adverse hypo- and hyperglycemic events.
    • Achieved high accuracy while maintaining computational efficiency suitable for wearable devices.

    Conclusions:

    • The Lightweight Sequential Transformer offers a practical and efficient solution for real-time blood glucose prediction in T1D management.
    • This model bridges the gap between high-performance AI and the constraints of wearable technology.
    • Enables more reliable and proactive management of Type 1 Diabetes through advanced predictive analytics on edge devices.