{On-Line Identification of a Patient-Disease Model for Mechanical Ventilation}

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 off-line data, which results in an information overload of the medical staff. Additionally, the underlying medical structure-function models are poorly understood or not applicable due to incomplete knowledge. We have developed an on-line identification 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 benefits from an exponential weighting function to classify more recent measurement values as more important. We have evaluated our identification scheme with real medical data sets showing the benets and drawbacks of our approach.