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A Hybrid Closed-Loop Blood Glucose Control Algorithm with a Safety Limiter Based on Deep Reinforcement Learning and

Shanyong Huang1, Yusheng Fu1, Shaowei Kong1

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology, Chengdu 611731, China.

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|January 27, 2026
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Summary

This study introduces a safer deep reinforcement learning (DRL) method for diabetes treatment by combining DRL with model predictive control (MPC). This hybrid approach improves blood glucose control in diabetic patients, enhancing safety and efficacy.

Keywords:
artificial pancreasblood glucose controlmodel predictive controlreinforcement learning

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Endocrinology

Background:

  • Managing blood glucose in diabetic patients is complex due to physiological variability and challenges with traditional insulin control algorithms.
  • Deep reinforcement learning (DRL) shows promise for diabetes treatment but faces safety concerns due to its trial-and-error nature.
  • Clinical application of DRL for diabetes management is hindered by the need for high safety standards.

Purpose of the Study:

  • To develop a safer and more effective insulin dosage control algorithm for diabetic patients.
  • To address the safety challenges of DRL in clinical diabetes treatment by integrating it with model predictive control (MPC).
  • To improve blood glucose regulation within the target range of 70-180 mg/dL.

Main Methods:

  • A hybrid control strategy combining Deep Reinforcement Learning (DRL) with Model Predictive Control (MPC).
  • Utilizing patient interaction data to train a blood glucose prediction model for MPC.
  • Incorporating a safety controller to restrict DRL actions and prevent unsafe blood glucose levels.
  • Validation using the FDA-approved UVA/Padova glucose kinetics simulator.

Main Results:

  • The proposed DRL-MPC hybrid model achieved 72.51% of time within the healthy blood glucose range for adult patients.
  • This represents a 2.54% improvement compared to the baseline model.
  • No increase in severe hyperglycemia or hypoglycemia events was observed, indicating enhanced safety.

Conclusions:

  • The combined DRL and MPC approach offers a safer and more effective method for automated insulin dosage control.
  • This hybrid strategy successfully addresses the safety limitations of pure DRL in clinical settings.
  • The findings represent a significant step towards the safe and reliable clinical application of AI in diabetes management.