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.

中文翻译：

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仅基于 CGM 时间序列的短期血糖预测

全球糖尿病流行凸显了保持良好血糖控制的重要性。血糖预测是糖尿病管理的一个基本方面，有助于实时决策。最近的研究引入了专注于长期血糖趋势预测的模型，这些模型不适合实时决策并导致响应延迟。相反，旨在响应即时血糖水平变化的模型无法全面分析血糖变异性。此外，当代研究通常会整合各种生理参数（例如胰岛素剂量、食物摄入量等），这不可避免地引起数据隐私问题。为了弥补这样的研究差距，我们提出了 TimeGlu——一种仅基于 CGM 时间序列数据的短期血糖预测的端到端管道。我们采用四种基线方法对模型的性能进行全面的比较分析。通过对两个对比数据集（CGM Glucose 和 Colas 数据集）进行大量实验，TimeGlu 实现了最先进的性能，无需患者提供额外的个人数据，为现实世界的糖尿病血糖管理提供有效指导。