ADAPTIVE MODELING FOR CONTROL OF GLYCEMIA IN CRITICALLY ILL PATIENTS

In this paper we propose an optimized adaptive ‘minimal’ modeling approach for predicting glycemia of critically ill patients and a corresponding Model based Predictive Control (MPC) setting for controlling glycemia. Re-estimations of the model, based on a real-life dataset from 19 critically ill patients, are performed every hour or every four hours by only considering recently passed data. The contributions of this study are the determination of the best dataset size for the re-estimations and the proposed MPC design. The results are satisfactory both in terms of forecasting ability and in qualitative controller performance.