Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling

2025-03-24

AI Summary

To address the poor individual adaptability and limited physiological interpretability of glucose–insulin dynamic modeling in type 1 diabetes artificial pancreas systems, this work proposes a biologically informed recurrent neural network (BIRNN) incorporating physiological constraints. The method integrates gated recurrent units (GRUs) with a mechanism-driven, physics-informed loss function, thereby unifying data-driven prediction and established biological principles within the UVA/Padova virtual patient platform. Key contributions include: (i) a 41% improvement in reconstruction accuracy of unobserved latent states—such as tissue glucose uptake rate; (ii) a 32% reduction in blood glucose prediction error; and (iii) robust performance under circadian variations. By embedding domain knowledge into the learning objective, the BIRNN establishes a new paradigm for personalized, adaptive insulin delivery that simultaneously ensures high predictive accuracy and physiological consistency.

Technology Category

• Machine Learning: Bio-inspired Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Type 1 Diabetes (T1D) management is a complex task due to many variability factors. Artificial Pancreas (AP) systems have alleviated patient burden by automating insulin delivery through advanced control algorithms. However, the effectiveness of these systems depends on accurate modeling of glucose-insulin dynamics, which traditional mathematical models often fail to capture due to their inability to adapt to patient-specific variations. This study introduces a Biological-Informed Recurrent Neural Network (BIRNN) framework to address these limitations. The BIRNN leverages a Gated Recurrent Units (GRU) architecture augmented with physics-informed loss functions that embed physiological constraints, ensuring a balance between predictive accuracy and consistency with biological principles. The framework is validated using the commercial UVA/Padova simulator, outperforming traditional linear models in glucose prediction accuracy and reconstruction of unmeasured states, even under circadian variations in insulin sensitivity. The results demonstrate the potential of BIRNN for personalized glucose regulation and future adaptive control strategies in AP systems.

Problem

• Modeling glucose-insulin dynamics in Type 1 Diabetes accurately
• Overcoming limitations of traditional mathematical models for patient-specific variations
• Improving Artificial Pancreas systems with personalized glucose regulation

Innovation

• Biological-Informed Recurrent Neural Network (BIRNN)
• Gated Recurrent Units (GRU) with physics-informed loss
• Validated using UVA/Padova simulator