A dynamic model with structured recurrent neural network to predict glucose-insulin regulation of type 1 diabetes mellitus

An artificial neural network (ANN) model for the prediction of glucose concentration in a glucose-insulin regulation system for type 1 diabetes mellitus is developed and validated by using the Continuous Glucose Monitoring System (CGMS) data. This network consists of structured framework according to the compartmental structure of the Hovorka-Wilinska model (HWM), and an additional update scheme is also included, which can improve the prediction accuracy whenever new measurements are available. The model is tested on a real case, as well as long term prediction has been carried over an extended time horizon from 30 minutes to 4 hours, and the quality of prediction is assessed by examining the values of the four indexes. For instant, the overall Clarke error grid (CEG) Zone A value is up to 100% for the 30-min-ahead prediction horizon with update. Therefore, for practical purpose, our results indicate that the promising prediction performance can be achieved by our proposed structured recurrent neural network model (SRNNM).