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.

翻译：1型糖尿病（T1D）的管理因多种变异因素而成为一项复杂任务。人工胰腺（AP）系统通过先进的控制算法实现胰岛素自动输送，从而减轻了患者负担。然而，这些系统的有效性取决于对葡萄糖-胰岛素动态的精确建模，而传统数学模型由于无法适应患者特异性变化，往往难以准确捕捉这些动态。本研究提出了一种生物信息循环神经网络（BIRNN）框架以解决这些局限性。BIRNN采用门控循环单元（GRU）架构，并通过嵌入生理约束的物理信息损失函数进行增强，从而确保预测精度与生物学原理一致性之间的平衡。该框架使用商业UVA/Padova模拟器进行验证，即使在胰岛素敏感性存在昼夜节律变化的情况下，其在葡萄糖预测精度和未测量状态重建方面均优于传统线性模型。研究结果证明了BIRNN在人工胰腺系统中实现个性化葡萄糖调控及未来自适应控制策略的潜力。