Neural model of blood glucose level for Type 1 Diabetes Mellitus Patients

Abstract

This paper presents on-line blood glucose level modeling for Type 1 Diabetes Mellitus (T1DM) patients. The model is developed using a recurrent neural network trained with an extended Kalman filter based algorithm in order to develop an affine model, which captures the nonlinear behavior of the blood glucose metabolism. The goal is to derive an on-line dynamical mathematical model of the T1DM for the response of a patient to meal and subcutaneous insulin infusion. Simulation results are utilized for identification and for testing the applicability of the proposed scheme.