GARNN: An Interpretable Graph Attentive Recurrent Neural Network for
  Predicting Blood Glucose Levels via Multivariate Time Series
Chengzhe Piao Taiyu Zhu Stephanie E Baldeweg Paul Taylor Pantelis Georgiou Jiahao Sun Jun Wang Kezhi Li
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.