Precise glucose level monitoring is critical for people with diabetes to avoid serious complications. While there are several methods for continuous glucose level monitoring, research on maintenance devices is limited. To mitigate the gap, we provide a novel neural control system for continuous glucose monitoring and management that uses differential predictive control. Our approach, led by a sophisticated neural policy and differentiable modeling, constantly adjusts insulin supply in real-time, thereby improving glucose level optimization in the body. This end-to-end method maximizes efficiency, providing personalized care and improved health outcomes, as confirmed by empirical evidence. Code and data are available at: \url{https://github.com/azminewasi/NeuralCGMM}.

翻译：精确的血糖水平监测对于糖尿病患者避免严重并发症至关重要。尽管已有多种连续血糖监测方法，但针对维持装置的研究仍较为有限。为弥补这一差距，我们提出了一种采用差分预测控制的新型神经控制系统，用于连续血糖监测与管理。该方法以精密的神经策略和可微分建模为核心，能够实时动态调节胰岛素供给，从而优化体内的血糖水平调控。这种端到端的方法最大限度地提升了效率，提供个性化护理并改善健康预后，相关实证研究已证实其有效性。代码与数据公开于：\url{https://github.com/azminewasi/NeuralCGMM}。