The global diabetes epidemic highlights the importance of maintaining good glycemic control. Glucose prediction is a fundamental aspect of diabetes management, facilitating real-time decision-making. Recent research has introduced models focusing on long-term glucose trend prediction, which are unsuitable for real-time decision-making and result in delayed responses. Conversely, models designed to respond to immediate glucose level changes cannot analyze glucose variability comprehensively. Moreover, contemporary research generally integrates various physiological parameters (e.g. insulin doses, food intake, etc.), which inevitably raises data privacy concerns. To bridge such a research gap, we propose TimeGlu -- an end-to-end pipeline for short-term glucose prediction solely based on CGM time series data. We implement four baseline methods to conduct a comprehensive comparative analysis of the model's performance. Through extensive experiments on two contrasting datasets (CGM Glucose and Colas dataset), TimeGlu achieves state-of-the-art performance without the need for additional personal data from patients, providing effective guidance for real-world diabetic glucose management.

翻译：全球糖尿病流行凸显了维持良好血糖控制的重要性。血糖预测是糖尿病管理的核心环节，有助于实现实时决策。近期研究引入了侧重于长期血糖趋势预测的模型，但这些模型不适用于实时决策，且会导致响应延迟。相反，旨在应对即时血糖波动的模型则无法全面分析血糖变异性。此外，当前研究通常整合多种生理参数（如胰岛素剂量、食物摄入等），这不可避免地引发了数据隐私问题。为弥补这一研究空白，我们提出TimeGlu——一种仅基于CGM时间序列数据进行短期血糖预测的端到端流程。我们实现了四种基线方法，对模型性能进行全面的比较分析。通过在两个具有对比性的数据集（CGM Glucose和Colas数据集）上进行广泛实验，TimeGlu在无需患者额外个人数据的情况下实现了最先进的性能，为现实世界中的糖尿病血糖管理提供了有效指导。