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Document type:
Report / Forschungsbericht
Author(s):
Steven Ganzert; Stefan Kramer; Knut Möller; Daniel Steinmann; Josef Guttmann
Title:
Prediction of Mechanical Lung Parameters Using Gaussian Process Models
Abstract:
Mechanical ventilation can cause severe lung damage by inadequate adjustment of the ventilator. We introduce a Machine Learning approach to predict the pressure-dependent, non-linear lung compliance, a crucial parameter to estimate lung protective ventilation settings. Features were extracted by fitting a generally accepted lumped parameter model to time series data obtained from ARDS (adult respiratory distress syndrome) patients. Numerical prediction was performed by use of Gaussian processes,...     »
Keywords:
Gaussian Processes; lung protective mechanical ventilation; lung compliance; ARDS (adult respiratory distress syndrome)
Year:
2009
Pages:
11
Language:
de
Format:
Text
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