Blood glucose (BG) prediction is an effective approach to avoid hyper- and hypoglycemia, and achieve intelligent glucose management for patients with type 1 or serious type 2 diabetes. Recent studies have tended to adopt deep learning networks to obtain improved prediction models and more accurate prediction results, which have often required significant quantities of historical continuous glucose-monitoring (CGM) data. However, for new patients with limited historical dataset, it becomes difficult to establish an acceptable deep learning network for glucose prediction. Consequently, the goal of this study was to design a novel prediction framework with instance-based and network-based deep transfer learning for cross-subject glucose prediction based on segmented CGM time series. Taking the effects of biodiversity into consideration, dynamic time warping (DTW) was applied to determine the proper source domain dataset that shared the greatest degree of similarity for new subjects. After that, a network-based deep transfer learning method was designed with cross-domain dataset to obtain a personalized model combined with improved generalization capability. In a case study, the clinical dataset demonstrated that, with additional segmented dataset from other subjects, the proposed deep transfer learning framework achieved more accurate glucose predictions for new subjects with type 2 diabetes.

血糖（BG）预测是一种避免高血糖和低血糖并为1型或2型严重糖尿病患者实现智能化血糖管理的有效方法。近期的研究倾向于采用深度学习网络来获得改进的预测模型和更准确的预测结果，而这通常需要大量的历史连续葡萄糖监测（CGM）数据。但是，对于历史数据集有限的新患者，很难建立可接受的深度学习网络来进行葡萄糖预测。因此，本研究的目的是设计一个基于实例和基于网络的深度转移学习的新颖预测框架，用于基于分段CGM时间序列的跨学科血糖预测。考虑到生物多样性的影响，动态时间规整（DTW）用于确定适当的源域数据集，该数据集对新主题具有最大的相似度。之后，利用跨域数据集设计了一种基于网络的深度转移学习方法，以获得结合了通用化能力的个性化模型。在一个案例研究中，临床数据集证明，与其他受试者的其他细分数据集相比，拟议的深度转移学习框架为2型糖尿病的新受试者提供了更准确的葡萄糖预测。设计了一种基于网络的深度转移学习方法，该方法具有跨域数据集，以获得结合了改进的泛化能力的个性化模型。在一个案例研究中，临床数据集证明，与其他受试者的其他细分数据集相比，拟议的深度转移学习框架为2型糖尿病的新受试者提供了更准确的葡萄糖预测。设计了一种基于网络的深度转移学习方法，该方法具有跨域数据集，以获得结合了改进的泛化能力的个性化模型。在一个案例研究中，临床数据集证明，与其他受试者的其他细分数据集相比，拟议的深度转移学习框架为2型糖尿病的新受试者提供了更准确的葡萄糖预测。