Rapid Model Identification for Online Glucose Prediction of New Subjects With Type 1 Diabetes Using Model Migration Method

Abstract

Online prediction of glucose concentration is of importance for blood glucose control in diabetes. For conventional modeling methods, model identification has to be repeated with sufficient data collected for each subject. This may cause repetitive cost and burden for patients and clinician and requires a lot of modeling efforts. Here, a rapid model development strategy is proposed using the idea of model migration for new subjects. First, a base model is obtained which can be empirically identified from any subject or constructed by priori knowledge. Then parameters of inputs in the base model are properly revised based on a small amount of data from new subjects. These issues are investigated by developing auto-regressive models with exogenous inputs (ARX) based on thirty in silico subjects. Some important issues relating to model adjustment performance are also checked, referring to the data used for model parameter adjustment and the interaction of two inputs, etc. The rapid modeling method is compared with subject-dependent models developed based on a large number of data with respect to on-line short-term (30min) glucose prediction accuracy.