Seasonal Local Models for Glucose Prediction in Type 1 Diabetes

: Linear empirical dynamic models have been widely used for blood glucose (BG) prediction and risks prevention in people with type 1 diabetes. More accurate BG prediction models with longer prediction horizon (PH) are desirable to enable warnings to patients about imminent BG changes with enough time to take corrective actions. In this study, a BG prediction method is developed by integrating the predictions of a set of seasonal local models (each of them corresponding to different glucose profiles observed along historical data). In the modeling step, the number of sets and their corresponding glucose profiles characteristics are obtained by clustering techniques (Fuzzy C-Means). Then, Box-Jenkins methodology is used to identify a seasonal model for each set. Finally, BG predictions of local models are integrated using different techniques. The proposed method is tested by using 18 60-h closed-loop experiments (including different exercise types and artificial pancreas strategies) and achieving mean absolute percentage error (MAPE) of 2.94%, 3.89%, 5.41%, 6.29% and 8.66% for 15-, 30-, 45-, 60-, and 90-min PHs, respectively.