Back to Search Start Over

Prediction of postprandial blood glucose under uncertainty and intra-patient variability in type 1 diabetes: a comparative study of three interval models

Authors :
Universitat Politècnica de València. Instituto Universitario de Automática e Informática Industrial - Institut Universitari d'Automàtica i Informàtica Industrial
Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica
Generalitat de Catalunya
Ministerio de Ciencia e Innovación
García Jaramillo, Maira Alejandra
Calm, R.
Bondía Company, Jorge
Vehí, J.
Universitat Politècnica de València. Instituto Universitario de Automática e Informática Industrial - Institut Universitari d'Automàtica i Informàtica Industrial
Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica
Generalitat de Catalunya
Ministerio de Ciencia e Innovación
García Jaramillo, Maira Alejandra
Calm, R.
Bondía Company, Jorge
Vehí, J.
Publication Year :
2012

Abstract

The behavior of three insulin action and glucose kinetics models was assessed for an insulin therapy regime in the presence of patient variability. For this purpose, postprandial glucose in patients with type 1 diabetes was predicted by considering intra- and inter-patient variability using modal interval analysis. Equations to achieve optimal prediction are presented for models 1, 2 and 3, which are of increasing complexity. The model parameters were adjusted to reflect the “same” patient in the presence of variability. The glucose response envelope for model 1, the simplest insulin–glucose model assessed, included the responses of the other two models when a good fit of the model parameters was achieved. Thus, under variability, simple glucose–insulin models may be sufficient to describe patient dynamics in most situations.

Details

Database :
OAIster
Notes :
TEXT, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1006870295
Document Type :
Electronic Resource