Insulin dosage optimization based on prediction of postprandial glucose excursions under uncertain parameters and food intake.
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.
García-Jaramillo
M
M
Institut d'Informatica i Aplicacions, University of Girona, Campus de Montilivi, Edifici P4, 17071 Girona, Spain.
Calm
R
R
Bondia
J
J
Tarín
C
C
Vehí
J
J
D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy* / Q000378:metabolism; D004435:Eating; D006801:Humans; D007328:Insulin / Q000008:administration & dosage*; D019518:Postprandial Period
D001786:Blood Glucose; D007328:Insulin