Conference Contribution

Glycemic Trend Prediction Using Empirical Model Identification

Marzia Cescon, Rolf Johansson

Abstract

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.

Keywords

subspace-based identification, biological systems


In Proc. Joint 48th IEEE Conference on Decision and Control & Chinese Control Conference (CDC2009 & CCC 2009), Shanghai, China, December 16-18, 2009, pp. 3501-3506 , December 2009. (CDC2009 & CCC 2009).

 
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