Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling
Journal:
arXiv
Published Date:
Mar 24, 2025
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.
