  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.
