用生物约束的神经网络，更准预测糖尿病患者血糖变化。
Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling
- 结合生理规律设计神经网络损失函数，提升模型可靠性。
- 在模拟器中血糖预测误差比传统模型降低18.7%。
- 适合个性化胰岛素调控系统开发，尤其对敏感度波动者有用。
1型糖尿病管理因个体差异复杂而困难。人工胰腺系统通过先进控制算法自动化胰岛素输送，减轻患者负担。但其效果依赖于对葡萄糖-胰岛素动态的精准建模，传统数学模型因难以适应个体差异而表现受限。本研究提出生物信息引导的循环神经网络（BIRNN），采用门控循环单元（GRU）结构，并引入嵌入生理约束的物理信息损失函数，兼顾预测精度与生物学合理性。在商用UVA/Padova模拟器上验证，BIRNN在血糖预测准确性和未测量状态重构方面均优于传统线性模型，即使在胰岛素敏感性昼夜变化条件下仍表现优异。结果表明，BIRNN在个性化血糖调控和未来自适应控制策略中具有潜力。
原文摘要 · Abstract (English)
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.
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