Equation discovery for model identification in respiratory mechanics of the mechanically ventilated human lung

Authors:
Steven Ganzert;Josef Guttmann;Daniel Steinmann;Stefan Kramer
Affiliations:
Institut für Informatik I12, Technische Universität München, München, Germany;Department of Anesthesiology and Critical Care Medicine, University Medical Center, Freiburg, Freiburg, Germany;Department of Anesthesiology and Critical Care Medicine, University Medical Center, Freiburg, Freiburg, Germany;Institut für Informatik I12, Technische Universität München, München, Germany
Venue:
DS'10 Proceedings of the 13th international conference on Discovery science
Year:
2010

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Abstract

Lung protective ventilation strategies reduce the risk of ventilator associated lung injury. To develop such strategies, knowledge about mechanical properties of the mechanically ventilated human lung is essential. This study was designed to develop an equation discovery system to identify mathematical models of the respiratory system in time-series data obtained from mechanically ventilated patients. Two techniques were combined: (i) the usage of declarative bias to reduce search space complexity and inherently providing the processing of background knowledge. (ii) A newly developed heuristic for traversing the hypothesis space with a greedy, randomized strategy analogical to the GSAT algorithm. In 96.8% of all runs the applied equation discovery system was capable to detect the well-established equation of motion model of the respiratory system in the provided data. We see the potential of this semi-automatic approach to detect more complex mathematical descriptions of the respiratory system from respiratory data.