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Probabilistic Patient Monitoring with Multivariate, Multimodal Extreme Value Theory

  • Samuel Hugueny
  • David A. Clifton
  • Lionel Tarassenko
Part of the Communications in Computer and Information Science book series (CCIS, volume 127)

Abstract

Conventional patient monitoring is performed by generating alarms when vital signs exceed pre-determined thresholds, but the false-alarm rate of such monitors in hospitals is so high that alarms are typically ignored. We propose a principled, probabilistic method for combining vital signs into a multivariate model of patient state, using extreme value theory (EVT) to generate robust alarms if a patient’s vital signs are deemed to have become sufficiently “extreme”. Our proposed formulation operates many orders of magnitude faster than existing methods, allowing on-line learning of models, leading ultimately to patient-specific monitoring.

Keywords

Patient monitoring Extreme value theory Extreme value distributions Density estimation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Samuel Hugueny
    • 1
  • David A. Clifton
    • 1
  • Lionel Tarassenko
    • 1
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordU.K.

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