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Neural Control System for Continuous Glucose Monitoring and Maintenance

Azmine Toushik Wasi

TL;DR

The paper tackles autonomous insulin delivery for diabetes by framing continuous glucose monitoring and maintenance as a closed-loop control problem. It deploys Differentiable Predictive Control (DPC) to learn a neural policy $u_k = \pi_{\theta}(g_k,R)$ that approximates an explicit MPC within a differentiable glucose-insulin model, enabling offline end-to-end optimization. The framework, NeuralCGMM, uses a white-box state-space model $g_{k+1} = A g_k + B u_k + E d_k$, outputs $u_k$ via a neural network, and optimizes a multi-term loss balancing tracking, smoothness, and constraints, via SGD. Experiments on synthetic data show the policy adapts to changing constraints and maintains glucose within reference ranges while respecting safety limits, suggesting potential for scalable, personalized CGM maintenance that requires clinical validation.

Abstract

Precise glucose level monitoring is critical for people with diabetes to avoid serious complications. While there are several methods for continuous glucose level monitoring, research on maintenance devices is limited. To mitigate the gap, we provide a novel neural control system for continuous glucose monitoring and management that uses differential predictive control. Our approach, led by a sophisticated neural policy and differentiable modeling, constantly adjusts insulin supply in real-time, thereby improving glucose level optimization in the body. This end-to-end method maximizes efficiency, providing personalized care and improved health outcomes, as confirmed by empirical evidence. Code and data are available at: \url{https://github.com/azminewasi/NeuralCGMM}.

Neural Control System for Continuous Glucose Monitoring and Maintenance

TL;DR

The paper tackles autonomous insulin delivery for diabetes by framing continuous glucose monitoring and maintenance as a closed-loop control problem. It deploys Differentiable Predictive Control (DPC) to learn a neural policy that approximates an explicit MPC within a differentiable glucose-insulin model, enabling offline end-to-end optimization. The framework, NeuralCGMM, uses a white-box state-space model , outputs via a neural network, and optimizes a multi-term loss balancing tracking, smoothness, and constraints, via SGD. Experiments on synthetic data show the policy adapts to changing constraints and maintains glucose within reference ranges while respecting safety limits, suggesting potential for scalable, personalized CGM maintenance that requires clinical validation.

Abstract

Precise glucose level monitoring is critical for people with diabetes to avoid serious complications. While there are several methods for continuous glucose level monitoring, research on maintenance devices is limited. To mitigate the gap, we provide a novel neural control system for continuous glucose monitoring and management that uses differential predictive control. Our approach, led by a sophisticated neural policy and differentiable modeling, constantly adjusts insulin supply in real-time, thereby improving glucose level optimization in the body. This end-to-end method maximizes efficiency, providing personalized care and improved health outcomes, as confirmed by empirical evidence. Code and data are available at: \url{https://github.com/azminewasi/NeuralCGMM}.
Paper Structure (16 sections, 7 equations, 5 figures, 1 table)

This paper contains 16 sections, 7 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. NeuralCGMM Method and Experiments
  4. Conclusion
  5. Related Works and Motivation
  6. Continuous Glucose Monitoring and Maintenance
  7. Differentiable Predictive Control
  8. Methodology in Depth
  9. Differentiable Predictive Control
  10. White-box System Model with Control Policy
  11. Experiments and Implementation Details
  12. Parameters
  13. Dataset Generation
  14. Test
  15. Figures
  16. ...and 1 more sections

Figures (5)

  • Figure 1: (a) Continuous Glucose Monitoring and Maintenance System; (b) Model Data Flow; (c) Glucose Level Output. (Full-size figures are available in Appendix \ref{['app:figures']})
  • Figure 2: Close Loop System Model.
  • Figure 3: Test Output Visualization.
  • Figure 4: Continuous Glucose Monitoring and Maintenance System
  • Figure 5: Model Data Flow for Continuous Glucose Monitoring and Maintenance System