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.

中文翻译：

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CrossGP：排除生理信息的跨日血糖预测

糖尿病患者数量的不断增加是当今社会的一个严重问题，对人民健康和国家财政支出产生重大负面影响。由于糖尿病可能发展成潜在的严重并发症，因此对糖尿病患者进行早期血糖预测对于及时就医至关重要。现有的血糖预测方法通常利用患者的隐私数据（例如年龄、性别、种族）和生理参数（例如血压、心率）作为血糖预测的参考特征，这不可避免地导致隐私保护问题。此外，这些模型通常侧重于长期（基于每月）或短期（基于分钟）预测。由于外部不确定性会对血糖值产生很大影响，长期预测方法普遍不准确，而短期预测方法又无法提供及时的医疗指导。基于上述问题，我们提出了CrossGP，一种新颖的机器学习框架，用于仅基于患者的外部活动而不涉及任何生理参数来预测跨日血糖。同时，我们实现了三个基线模型进行比较。在 Anderson 的数据集上进行的大量实验有力地证明了 CrossGP 的卓越性能，并证明了其在未来现实生活中应用的潜力。