[论文解读] GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes

Maxime De Bois, Mehdi Ammi+1 more|arXiv (Cornell University)|Jun 29, 2020

Diabetes Management and Research参考文献 114被引用 36

一句话总结

本文对用于1型糖尿病的个性化葡萄糖预测模型进行了评审与基准测试。

ABSTRACT

Due to the sensitive nature of diabetes-related data, preventing them from being shared between studies, progress in the field of glucose prediction is hard to assess. To address this issue, we present GLYFE (GLYcemia Forecasting Evaluation), a benchmark of machine-learning-based glucose-predictive models. To ensure the reproducibility of the results and the usability of the benchmark in the future, we provide extensive details about the data flow. Two datasets are used, the first comprising 10 in-silico adults from the UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS) and the second being made of 6 real type-1 diabetic patients coming from the OhioT1DM dataset. The predictive models are personalized to the patient and evaluated on 3 different prediction horizons (30, 60, and 120 minutes) with metrics assessing their accuracy and clinical acceptability. The results of nine different models coming from the glucose-prediction literature are presented. First, they show that standard autoregressive linear models are outclassed by kernel-based non-linear ones and neural networks. In particular, the support vector regression model stands out, being at the same time one of the most accurate and clinically acceptable model. Finally, the relative performances of the models are the same for both datasets. This shows that, even though data simulated by T1DMS are not fully representative of real-world data, they can be used to assess the forecasting ability of the glucose-predictive models. Those results serve as a basis of comparison for future studies. In a field where data are hard to obtain, and where the comparison of results from different studies is often irrelevant, GLYFE gives the opportunity of gathering researchers around a standardized common environment.

研究动机与目标

• 识别并分类现有的用于1型糖尿病的个性化葡萄糖预测模型。
• 评估用于1型糖尿病葡萄糖预测的建模方法。
• 评估跨研究的性能衡量与基准测试实践。
• 强调 standardized 基准测试的差距、挑战与机会。

提出的方法

• 对用于1型糖尿病的个性化葡萄糖预测模型进行结构化文献综述。
• 提出或应用基准标准以比较建模方法。
• 分析在被引研究中使用的方法与性能指标。
• 讨论方法学的优点、局限性与可重复性考量。

实验结果

研究问题

• RQ1 用于1型糖尿病的个性化葡萄糖预测的主要建模方法有哪些？
• RQ2 不同模型在不同数据集、设置和评价指标下的表现如何？
• RQ3 当前基准测试实践中存在哪些共性局限和差距？
• RQ4 有哪些建议可以改善葡萄糖预测模型的公平性和可重复性基准测试？

主要发现

• 为1型糖尿病的个性化葡萄糖预测调查了多种建模方法。
• 模型在数据集和评估指标上的表现存在差异。
• 基准测试实践揭示可重复性与可比性方面的不一致性与差距。
• 常见挑战包括数据质量、个性化和特征工程方面的考虑。
• 该工作确定了标准化基准框架与未来研究方向的机会。