Short-Term Prediction of Blood Glucose Concentration using Interval Probabilistic Models
Sprache des Titels:
22nd Mediterranean Conference on Control and Automation
Insulin therapy of type 1 diabetes is essentially a
case of feed-forward control in which a wrong decision can
significantly affect or even harm the patient. Accordingly, the
quality of the model used to predict the effect of an insulin
subministration would have a paramount importance.
Unfortunately, for many reasons, among them the very high
interpatient and intrapatient variability and the strong
influence of stochastic elements, no sufficiently reliable patienttunable
models are available to predict precisely the blood
glucose (BG) value development especially after meals. Against
this background, attempts have been done to develop interval
estimations and predictions instead of single values.
This paper suggests using interval models based on
physiology and describing the development of the BG in terms
of transition probabilities. To this end, we use Gaussian
Mixture Models (GMM) and data from real patients. The
evaluation shows that the proposed approach is able to provide
a good to very good prediction for time ranges of 10 to 30
minutes, both during night and day, with or without meals,
while never producing a prediction which could lead to a
potentially dangerous decision for the patient.