Effect of meal intake on the quality of empirical dynamic models for Type 1 Diabetes

Abstract

A model-based controller for artificial pancreas requires a model that is able to predict future glucose trends precisely. To quantify the effect of meal intake on the quality of empirical dynamic models (EDM), changing meal conditions (e.g., the meal amounts and times variation, individual differences) were simulated to generate data. Both single-input single-output (SISO) and multi-input single-output (MISO) EDM were identified and evaluated via model identification technology. The prediction accuracy of these models varies significantly within a subject and between subjects due to the different variation of meal amounts, and the additional afternoon snack and meal times shift have the greatest influence on these models. The prediction accuracy of MISO models are worse than that of SISO models under the changing meal condition.