On-Line Identi cation of a Patient-Disease Model for Mechanical Ventilation

Monitoring and therapy planning in real-world environments highly depend on good patientdisease models. The improvement of the technical equipment in modern intensive care units enables a huge number of onand o -line data, which results in an information overload of the medical sta . Additionally, the underlying medical structure-function models are poorly understood or not applicable due to incomplete knowledge. We have developed an on-line identi cation scheme, which utilizes a priori knowledge as well as on-line measurements to identify the parameters of a disease model for mechanically ventilated newborn infants. The scheme bene ts from an exponential weighting function to classify more recent measurement values as more important. We have evaluated our identi cation scheme with real medical data sets showing the bene ts and drawbacks of our approach.