Prediction of postprandial glucose excursions in type 1 diabetes using control-oriented process models

Reliable prediction of future blood glucose (BG) values is of high relevance for diabetes patients, since it enables the use of predictive glucose alarms (warning the patient about impending situations with dangerously low or high BG), as well as of model-based algorithms for smart glucose control. Control-oriented graybox process models have proven very suitable for such tasks, especially when identified on data from clinical trials under well-defined conditions. The current paper analyzes how such models can also be reliably parametrized using outpatient data of patients on multiple daily injection (MDI) therapy. A dedicated preprocessing algorithm is presented to look for suitable (i.e. complete and sensible) data segments that allow for a reliable system identification. The focus of the current paper is on the prediction of postprandial glucose trajectories, more specifically on predictions made exactly at the time of meal ingestion. This corresponds to a particularly challenging task, but one with high importance for the modelbased optimization of insulin doses. It is demonstrated that the identified process models are a suitable choice for predicting such postprandial glucose excursions.