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Dawei Shi1, Eyal Dassau1, Francis J Doyle1

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This study presents a new data-driven method to adapt artificial pancreas (AP) systems for type 1 diabetes management. The adaptive algorithm improves glucose control in simulated home environments, even with lifestyle changes.

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Area of Science:

  • Biomedical Engineering
  • Endocrinology
  • Computational Biology

Background:

  • Long-term artificial pancreas (AP) use necessitates adaptive algorithms for evolving patient needs.
  • Automated insulin delivery requires continuous learning to manage type 1 diabetes effectively.

Purpose of the Study:

  • To introduce a data-driven method for adapting AP parameters in home environments.
  • To enhance glucose management through personalized AP algorithm adjustments.

Main Methods:

  • A two-phase Bayesian optimization assisted parameter learning algorithm was developed.
  • The algorithm adapts basal and carbohydrate-ratio profiles, and feedback control parameters.
  • Evaluation utilized the FDA-accepted UVA/Padova type 1 diabetes mellitus simulator with 111 adult subjects.

Main Results:

  • The proposed method robustly adapted AP parameters across three simulated scenarios.
  • Improved glycemic regulation was observed, increasing time in the euglycemic range [70, 180] mg/dl.
  • No increased risk of hypoglycemia (time below 70 mg/dl) was detected.

Conclusions:

  • The data-driven AP adaptation method offers robust glucose management for type 1 diabetes.
  • This approach supports personalized and effective automated insulin delivery in real-world settings.
  • The adaptive algorithm shows promise for long-term AP system usability and patient outcomes.