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

Monitoring and therapy planning in real-world environments highly depend on good patient-disease models. The improvement of the technical equipment in modern intensive care units enables a huge number of on-and oo-line data, which results in an information overload of the medical staa. Additionally, the underlying medical structure-function models are poorly understood or not applicable due to incomplete knowledge. We have developed an on-line iden-tiication 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 beneets from an exponential weighting function to classify more recent measurement values as more important. We have evaluated our iden-tiication scheme with real medical data sets showing the beneets and drawbacks of our approach .