title: Blood glucose level neural model for type 1 diabetes mellitus patients.
abstract: This paper deals with the 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 a dynamical mathematical model for the T1DM as the response of a patient to meal and subcutaneous insulin infusion. Experimental data given by continuous glucose monitoring system is utilized for identification and for testing the applicability of the proposed scheme to T1DM subjects.
authors: CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. las Aguilas, C.P. 45080, Zapopan, Jalisco, Mexico. almayalanis@gmail.com
authors: Alma Y
authors: AY
authors: Alanis
authors: Blanca S
authors: BS
authors: Leon
authors: Edgar N
authors: EN
authors: Sanchez
authors: Eduardo
authors: E
authors: Ruiz-Velazquez
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000188:drug therapy; D006801:Humans; D007328:Insulin / Q000627:therapeutic use; D008954:Models, Biological; D008962:Models, Theoretical; D016571:Neural Networks, Computer
chemical_list: D001786:Blood Glucose; D007328:Insulin