Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Identifying mathematical models of the mechanically ventilated lung using equation discovery
 Dössel, O. ; International Union for Physical and Engineering Sciences in Medicine IUPESM: World Congress on Medical Physics and Biomedical Engineering 2009. Vol.4: Image processing, biosignal processing, modelling and simulation, biomechanics. Part 2 : September 7  12, 2009, Munich, Germany, WC 2009; 11th international congress of the IUPESM Berlin: Springer, 2009 (IFMBE proceedings 25/4) ISBN: 9783642038815 ISBN: 9783642038822 pp.15241527 
 World Congress on Medical Physics and Biomedical Engineering <2009, München> International Union for Physical and Engineering Sciences in Medicine (International Congress) <11, 2009, München> 

 English 
 Conference Paper 
 Fraunhofer IAIS () 
Abstract
Mechanical ventilation is the livesaving therapy in intensive care medicine by all means. Nevertheless, it can induce severe mechanical stress to the lung, which generally impairs the outcome of the therapy. To reduce the risk of a ventilator induced lung injury (VILI), lung protective ventilation is essential, especially for patients with a previous medical history like the adult respiratory distress syndrome (ARDS). The prerequisite for lung protective ventilation approaches is the knowledge about the physical behavior of the human lung under the condition of mechanical ventilation. This knowledge is commonly described by mathematical models. Diverse models have been introduced to represent particular aspects of mechanical characteristics of the lung. A commonly accepted general model is the equation of motion, which relates the airway pressure to the airflow and the volume applied by the ventilator and describes the influence of the distensibility and resistance of the respiratory system. Equation Discovery systems extract mathematical models from observed time series data. To reduce the vast search space associated with this task, the LAGRAMGEsystem introduced the application of declarative bias in Equation Discovery, which furthermore allows the presentation of domain specific knowledge. We introduce a modification of this system and apply it to data obtained during mechanical ventilation of ARDSpatients. We experimentally validate the effectiveness of our approach and show that the equation of motion model can automatically be rediscovered from realworld data.