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.

基于 CGM 时间序列数据的短期血糖预测模型 TimeGlu，在没有额外个人数据的情况下，在实际世界中提供了有效的糖尿病血糖管理指导。

基于 CGM 时间序列的短期血糖预测

Toward Short-Term Glucose Prediction Solely Based on CGM Time Series

The increasing number of diabetic patients is a serious issue in society
today, which has significant negative impacts on people's health and the
country's financial expenditures. Because diabetes may develop into potential
serious complications, early glucose prediction for diabetic patients is
necessary for timely medical treatment. Existing glucose prediction methods
typically utilize patients' private data (e.g. age, gender, ethnicity) and
physiological parameters (e.g. blood pressure, heart rate) as reference
features for glucose prediction, which inevitably leads to privacy protection
concerns. Moreover, these models generally focus on either long-term
(monthly-based) or short-term (minute-based) predictions. Long-term prediction
methods are generally inaccurate because of the external uncertainties that can
greatly affect the glucose values, while short-term ones fail to provide timely
medical guidance. Based on the above issues, we propose CrossGP, a novel
machine-learning framework for cross-day glucose prediction solely based on the
patient's external activities without involving any physiological parameters.
Meanwhile, we implement three baseline models for comparison. Extensive
experiments on Anderson's dataset strongly demonstrate the superior performance
of CrossGP and prove its potential for future real-life applications.

基于患者的外部活动而无需涉及任何生理参数，我们提出了 CrossGP，这是一种新颖的机器学习框架，用于跨日血糖预测。通过与三种基准模型进行比较，对 Anderson 的数据集进行了大量实验，强烈证明了 CrossGP 的卓越性能，并证明了其在未来实际应用中的潜力。

CrossGP: 排除生理信息的跨日血糖预测

CrossGP: Cross-Day Glucose Prediction Excluding Physiological  Information

People with diabetes must carefully monitor their blood glucose levels,
especially after eating. Blood glucose regulation requires a proper combination
of food intake and insulin boluses. Glucose prediction is vital to avoid
dangerous post-meal complications in treating individuals with diabetes.
Although traditional methods, such as artificial neural networks, have shown
high accuracy rates, sometimes they are not suitable for developing
personalised treatments by physicians due to their lack of interpretability. In
this study, we propose a novel glucose prediction method emphasising
interpretability: Interpretable Sparse Identification by Grammatical Evolution.
Combined with a previous clustering stage, our approach provides finite
difference equations to predict postprandial glucose levels up to two hours
after meals. We divide the dataset into four-hour segments and perform
clustering based on blood glucose values for the twohour window before the
meal. Prediction models are trained for each cluster for the two-hour windows
after meals, allowing predictions in 15-minute steps, yielding up to eight
predictions at different time horizons. Prediction safety was evaluated based
on Parkes Error Grid regions. Our technique produces safe predictions through
explainable expressions, avoiding zones D (0.2% average) and E (0%) and
reducing predictions on zone C (6.2%). In addition, our proposal has slightly
better accuracy than other techniques, including sparse identification of
non-linear dynamics and artificial neural networks. The results demonstrate
that our proposal provides interpretable solutions without sacrificing
prediction accuracy, offering a promising approach to glucose prediction in
diabetes management that balances accuracy, interpretability, and computational
efficiency.

该研究提出了一种新的葡萄糖预测方法，其中结合聚类方法和可解释的语法演化算法，生成有限差分方程，为餐后两小时内的餐后血糖水平提供预测，并通过可解释的表达式生成安全预测，同时提高了预测准确性。