aiqbench analysis
This study proposes a Biological-Informed Recurrent Neural Network (BIRNN) that integrates GRU architectures with physics-informed loss functions to better model glucose-insulin dynamics for Type 1 Diabetes management. By embedding physiological constraints directly into the training process, the framework achieves superior predictive accuracy and state reconstruction compared to traditional linear models, even under circadian variations. The approach offers a viable path toward more robust, personalized artificial pancreas systems by bridging the gap between data-driven learning and biological plausibility.
Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling
arXiv:2503.19158v3 Announce Type: replace 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.
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.
Accepted for publication in the proceedings of the Engineering Diabetes Technologies (EDT 2025). 7 pages, 2 figures and 1 table
Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
arXiv:2503.19158 [cs.LG]
(or arXiv:2503.19158v3 [cs.LG] for this version)
IFAC-PapersOnLine, 59(2), 2025, pp. 91-96
https://doi.org/10.1016/j.ifacol.2025.06.016 DOI(s) linking to related resources
Full text under the publisher's open licence · 196 words
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