Glycemic Trend Prediction Using Empirical Model Identification

In English Using methods of system identification and prediction, we investigate near-future prediction of individual specific T1DM blood glucose dynamics with the purpose of a decision-making tool development in diabetes treatment. Two strategies were approached: Firstly, Kalman estimators based on identified state-space models were designed; Secondly, direct identification of ARX- and ARMAX-based predictors was done.
Predictions over 30 minutes look-ahead were capable to track
glucose variation even in sensible ranges for estimation data,
but not on validation data.