Insulin dosage optimization based on prediction of postprandial glucose excursions under uncertain parameters and food intake

Authors:
M. García-Jaramillo;R. Calm;J. Bondia;C. Tarín;J. Vehí
Affiliations:
Institut d'Informatica i Aplicacions, University of Girona, Campus de Montilivi, Edifici P4, 17071 Girona, Spain;Institut d'Informatica i Aplicacions, University of Girona, Campus de Montilivi, Edifici P4, 17071 Girona, Spain;Instituto de Automática e Informática Industrial, Universidad Politécnica de Valencia, Camino de Vera, s/n, 46022 Valencia, Spain;Institute for System Dynamics, University of Stuttgart, Pfaffenwaldring 9 (3. OG), Stuttgart, Germany;Institut d'Informatica i Aplicacions, University of Girona, Campus de Montilivi, Edifici P4, 17071 Girona, Spain
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

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Abstract

Considering the difficulty in selecting correct insulin doses and the problem of hyper- and hypoglycemia episodes in type 1 diabetes, dosage-aid systems are very useful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as large intra-patient variability and food intake. In the present study, postprandial glucose is predicted considering this uncertain information using modal interval analysis. This approach calculates a safer prediction of possible hyper- and hypoglycemia episodes induced by insulin therapy for an individual patient's parameters and integrates this information into a dosage-aid system. Predictions of a patient's postprandial glucose at 5-h intervals are used to predict the risk for a given therapy. Then the insulin dose and injection-to-meal time with the lowest risk are calculated. The method has been validated for three different scenarios corresponding to preprandial glucose values of 100, 180 and 250mg/dl.