Paper Info
Title
A new insulin-glucose metabolic model of type 1 diabetes mellitus: An in silico study.
Abstract
Diabetes mellitus is a serious metabolic disease that threatens people's health. The artificial pancreas system (APS) has been generally considered as the ultimate cure of type 1 diabetes mellitus (T1DM). The simulation model of insulin-glucose metabolism is an essential part of an APS as it processes the measured glucose level and generates control signal to the insulin infusion system. This paper presents a new insulin-glucose metabolic model using model reduction methods applied to the popular but complex Cobelli's model. The performances of three different model reduction methods, namely Padé approximation, Routh approximation and system identification, are compared. The results of in silico simulation based on 30 virtual patients of three groups for adults, adolescents, and children show that the approximation error between this new model and the original Cobelli's model is so small that can be neglected. It can be concluded that the proposed simplified model can describe the insulin-glucose metabolism process rather accurately as well as can be easily implemented and integrated into an APS to make the APS technology more mature and closer to clinical use. The FPGA implementation, testing and further simplification possibility will be explored in the next stage of research.
Year
DOI
Venue
2015
10.1016/j.cmpb.2015.03.009
Computer Methods and Programs in Biomedicine
Keywords
Field
DocType
Insulin-glucose metabolism model,T1DM,Model reduction method
Computer vision,Artificial pancreas,Diabetes mellitus,Metabolic Model,Computer science,Simulation,Artificial intelligence,Insulin,Type 1 diabetes,System identification,Approximation error,Machine learning
Journal
Volume
Issue
ISSN
120
1
0169-2607
Citations 
PageRank 
References 
0
0.34
4
Authors
3
Name
Order
Citations
PageRank
Qiang Fang120840.74
Lei Yu231.42
Peng Li3919.61