Personalized Glucose Prediction with Clustering-Based Domain Adaptation for Type 2 Diabetes

Background and objective: For patients with type 2 diabetes (T2D), accurate prediction of blood glucose variations is essential for maintaining glycemic control, decreasing the occurrence of hypo/hyperglycemic events, and preventing diabetes complications. However, this is difficult to achieve due to high inter-individual variability, insufficient glucose data, and the complexity of glucose dynamics. To address these issues, inter-individual variability can be overcome by using domain adaptation to leverage multiple patient data for personalized deep modeling of glucose dynamics.Methods: In this work, a clustering-based domain adaptation method is proposed for personalized glucose prediction of T2D with insufficient data. Firstly, the multi-level clustering method is used to subtyping the heterogeneous group of patients with T2D into four homogenous subgroups to deal with the high inter-individual variability. Then, a domain adaptation prediction network is designed to overcome the challenges caused by insufficient historical data of the target patient through cross-patient knowledge transfer and obtain a personalized deep prediction model suitable for the target patient.Results: The effectiveness of the proposed method was evaluated in a clinical dataset containing continuous glucose monitoring (CGM) measurement records from 908 patients with T2D, each with only a small amount of data. The average prediction results showed that the root mean square error of the 30-minute prediction horizon was 14.961 mg/dL, and more than 94% of the predicted values were ‘clinically accurate’.Conclusions: The proposed personalized method can achieve an accurate glucose prediction for patients with T2D even if the target patient has only one-day historical CGM records.