Abstrakt
A multiple-steps ahead prediction of glucose level from real time continuous glucose monitoring system (RT-CGMS) device is presented. Both linear and nonlinear autoregressive models with exogenous inputs are used for the system identification.
Insulin and nutritional income are used as the exogenous inputs. To better represent the dynamical character of those external factors, simple compartment models are used providing a signal of the influence of the insulin and nutrition.
Those signals are used as inputs to regressive models. The main problem of adaptation of those compartment models to particular patient is solved using continuous particle swarm optimization algorithm.
The proposed approach is demonstrated on data from type I diabetic patients with RT-CGMS and insulin pump. The results provide the first step of creation of future mobile application for decision support of type 1 diabetics.