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

This study investigates how machine learning (ML) models can predict hospital
readmissions for diabetic patients fairly and accurately across different
demographics (age, gender, race). We compared models like Deep Learning,
Generalized Linear Models, Gradient Boosting Machines (GBM), and Naive Bayes.
GBM stood out with an F1-score of 84.3% and accuracy of 82.2%, accurately
predicting readmissions across demographics. A fairness analysis was conducted
across all the models. GBM minimized disparities in predictions, achieving
balanced results across genders and races. It showed low False Discovery Rates
(FDR) (6-7%) and False Positive Rates (FPR) (5%) for both genders.
Additionally, FDRs remained low for racial groups, such as African Americans
(8%) and Asians (7%). Similarly, FPRs were consistent across age groups (4%)
for both patients under 40 and those above 40, indicating its precision and
ability to reduce bias. These findings emphasize the importance of choosing ML
models carefully to ensure both accuracy and fairness for all patients. By
showcasing effectiveness of various models with fairness metrics, this study
promotes personalized medicine and the need for fair ML algorithms in
healthcare. This can ultimately reduce disparities and improve outcomes for
diabetic patients of all backgrounds.

通过比较深度学习、广义线性模型、梯度提升机（GBM）和朴素贝叶斯等模型，我们发现 GBM 在不同人口统计学特征（年龄、性别、种族）下，以 84.3% 的 F1 分数和 82.2% 的准确率准确预测糖尿病患者的住院再入院情况。GBM 在预测中减少了性别和种族之间的差异，其假发现率（FDR） (6-7%) 和假阳性率（FPR） (5%) 较低，且各年龄组（40 岁以下和 40 岁以上患者）的 FDR（4%）保持稳定，表明其精确性和减少偏见的能力。通过展示多种模型的准确度和公平性指标，该研究强调了在医疗保健中谨慎选择机器学习模型以确保准确性和公正性的重要性，从而促进个性化医学并确保公平的机器学习算法，最终减少糖尿病患者不同背景中的不平等，并改善结果。