Assessing a Personalized, Hybrid, and Generic Approach for Glucose Prediction in Type 1 Diabetes

This study investigates the potential of using generic, hybrid, and personalized neural network models for glucose prediction in individuals with Type 1 Diabetes (T1D). Data from 194 participants in the Wireless Innovations for Seniors with Diabetes Mellitus (WISDM) study, totaling over 46 million minutes of Continuous Glucose Monitoring (CGM), were used to develop and evaluate the models. A baseline reference model, Last Observation Carried Forward (LOCF), was also included for comparison. Models were trained using data from 70% of the participants and tested on the remaining 30%, with prediction horizons (PH) set at 30 and 60 minutes. At the 30-minute PH, the generic model achieved a Root Mean Square Error (RMSE) of 19.6 mg/dL and a Mean Absolute Relative Difference (MARD) of 9.2%. These results were slightly worse than those of the hybrid and personalized models, which yielded RMSEs of 19.4 mg/dL and 19.5 mg/dL, respectively, and MARDs of 9.6% for both. However, the differences were not statistically significant. At the 60-minute PH, the generic model showed the best performance, with an RMSE of 34.9 mg/dL and MARD of 16.2%. The hybrid and personalized models exhibited slightly higher RMSEs (35.3 mg/dL and 35.5 mg/dL, respectively) and MARDs (17.8% and 18.1%, respectively). These findings suggest that both generic and individualized models can provide satisfactory glucose forecasting results based solely on CGM data. Nonetheless, the potential benefits of individualized approaches deserve further investigation, particularly when substantial training data are available.