Neural network modeling and control of type 1 diabetes mellitus.

This paper presents a developed and validated dynamic simulation model of type 1 diabetes, that simulates the progression of the disease and the two term controller that is responsible for the insulin released to stabilize the glucose level. The modeling and simulation of type 1 diabetes mellitus is based on an artificial neural network approach. The methodology builds upon an existing rich database on the progression of type 1 diabetes for a group of diabetic patients. The model was found to perform well at estimating the next glucose level over time without control. A neural controller that mimics the pancreas secretion of insulin into the body was also developed. This controller is of the two term type: one stage is responsible for short-term and the other for mid-term insulin delivery. It was found that the controller designed predicts an adequate amount of insulin that should be delivered into the body to obtain a normalization of the elevated glucose level. This helps to achieve the main objective of insulin therapy: to obtain an accurate estimate of the amount of insulin to be delivered in order to compensate for the increase in glucose concentration.