In this paper we propose a neural network identification of a mathematical model called MINMOD, which describes the interactions between glucose and insulin in human subjects, in order to realize an adequate model for patients suffering from {\it Diabetes Mellitus} Type 2. The model has been tested on the basis of clinical data and it can correctly reproduce glucose and insulin reply and temporal evolution, according to experimental data test. Using neural networks, we can predict the glucose temporal evolution without invasive technique for patients, with the aim to determine the clinical effects to be made in case of pathological behaviors.
Neural network in modeling glucose-insulin behavior / Panella, Massimo; Barcellona, Francesco; Bersani, Alberto Maria. - STAMPA. - (2005), pp. 367-374. ((Intervento presentato al convegno 15th Italian Workshop on Neural Nets (WIRN VETRI 2004) tenutosi a Perugia, ITALY nel SEP 14-17, 2004. [10.1007/1-4020-3432-6_43].
Neural network in modeling glucose-insulin behavior
PANELLA, Massimo;BARCELLONA, FRANCESCO;BERSANI, Alberto Maria
2005
Abstract
In this paper we propose a neural network identification of a mathematical model called MINMOD, which describes the interactions between glucose and insulin in human subjects, in order to realize an adequate model for patients suffering from {\it Diabetes Mellitus} Type 2. The model has been tested on the basis of clinical data and it can correctly reproduce glucose and insulin reply and temporal evolution, according to experimental data test. Using neural networks, we can predict the glucose temporal evolution without invasive technique for patients, with the aim to determine the clinical effects to be made in case of pathological behaviors.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.