Prediction of Mechanical Lung Parameters Using Gaussian Process Models

Authors:
Steven Ganzert;Stefan Kramer;Knut Möller;Daniel Steinmann;Josef Guttmann
Affiliations:
Department of Experimental Anesthesiology, University Hospital Freiburg, Freiburg, Germany D-79106;Institut für Informatik / I12, Technische Universität München, Garching b. München, Germany D-85748;Department of Biomedical Engineering, Furtwangen University, Villingen-Schwenningen, Germany D-78054;Department of Experimental Anesthesiology, University Hospital Freiburg, Freiburg, Germany D-79106;Department of Experimental Anesthesiology, University Hospital Freiburg, Freiburg, Germany D-79106
Venue:
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Year:
2009

Citing 2
Cited 0

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

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Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning

Artificial Intelligence in Medicine

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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, a probabilistic, non-parametric modeling approach for non-linear functions.