DEVELOPMENT OF A CRITICALLY ILL PATIENT INPUT-OUTPUT MODEL

In this paper we apply system identification in order to build a model suitable for prediction of the glycemia levels of critically ill patients in the Intensive Care Unit. These patients typically show increased glycemia levels, and it has been shown that glycemia control by means of insulin therapy reduces morbidity and mortality. Based on a real-life dataset from 41 critically ill patients, an ARX model is estimated which captures the insulin effect on glycemia under different settings. The results are satisfactory both in terms of forecasting ability and in the clinical interpretation of the estimated coefficients.