Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been achieved by leveraging advanced deep learning methods to model multi-modal data, i.e., sensor data and self-reported event data, organised as multi-variate time series (MTS). However, these methods are mostly regarded as ``black boxes'' and not entirely trusted by clinicians and patients. In this paper, we propose interpretable graph attentive recurrent neural networks (GARNNs) to model MTS, explaining variable contributions via summarizing variable importance and generating feature maps by graph attention mechanisms instead of post-hoc analysis. We evaluate GARNNs on four datasets, representing diverse clinical scenarios. Upon comparison with twelve well-established baseline methods, GARNNs not only achieve the best prediction accuracy but also provide high-quality temporal interpretability, in particular for postprandial glucose levels as a result of corresponding meal intake and insulin injection. These findings underline the potential of GARNN as a robust tool for improving diabetes care, bridging the gap between deep learning technology and real-world healthcare solutions.

精确预测未来血糖水平能有效改善糖尿病患者的血糖管理，降低并发症发生率，提高生活质量。本研究提出了可解释的图注意力循环神经网络（GARNN）用于建模多变量时间序列数据，不仅在预测准确性上表现最佳，同时提供高质量的时间解释性，进而在改善糖尿病护理方面发挥了潜在作用。

GARNN: 一种可解释的图注意力循环神经网络用于预测多元时间序列中的血糖水平