Predictive modeling of glucose metabolism using free-living data of type 1 diabetic patients

The aim of this study is to model the blood glucose metabolism of type 1 diabetic patients using free-living data. The proposed method considers the effect of diet, medication and exercise on blood glucose levels. Compartmental models are used to quantify the absorption of subcutaneously administered insulin, the absorption of glucose from the gut following a meal, as well as the effects of exercise on plasma glucose and insulin dynamics. Compartmental analysis is combined with a glucose predictive model which employs Support Vector machines for Regression to estimate the subcutaneous glucose concentrations. The model is trained and tested on real data recorded from two type 1 diabetic patients. The results obtained demonstrate the ability of the model to predict glucose response with a sufficient accuracy.