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Bayesian Learning of the Gas Exchange Properties of the Lung for Prediction of Arterial Oxygen Saturation

  • David Murley
  • Stephen Rees
  • Bodil Rasmussen
  • Steen Andreassen
Conference paper
  • 442 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2780)

Abstract

This paper describes how real-time Bayesian learning of physiological model parameters is used to predict arterial oxygen saturation at the bedside. The efficacy of using these predictions as a decision support tool in a system for estimating gas exchange parameters of the lung (ALPE) was tested retrospectively. For the predictions to offer effective decision support they need to be accurate and safe. These qualities were tested for two patient groups, using two different test strategies for each group. The prediction accuracy when used in combination with the predictions’ safety margin was found to be adequate in all the test cases. Thus the method described can be used as the basis for effective model-based decision support in ALPE.

Keywords

Prediction Error Prediction Accuracy Test Strategy Arterial Oxygen Saturation Bayesian Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • David Murley
    • 1
  • Stephen Rees
    • 1
  • Bodil Rasmussen
    • 2
  • Steen Andreassen
    • 1
  1. 1.Center for Model Based Medical Decision Support SystemsAalborg UniversityAalborgDenmark
  2. 2.Department of AnaesthesiaAalborg HospitalAalborgDenmark

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