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Conference on Artificial Intelligence in Medicine in Europe

AIME 1997: Artificial Intelligence in Medicine pp 331–342Cite as

Temporal scenario recognition for intelligent patient monitoring

  • Temporal Reasoning and Planning
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1211))

Abstract

The recognition of high level clinical scenes as they are developing is fundamental in patient monitoring. In this paper, we propose a technique to recognize on the fly a session, i.e. the clinical process's evolution, by comparison to a predetermined set of scenarios, i.e. the possible behaviours for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of proximity is introduced to quantify the degree of matching between two temporal networks and used to select the best scenario fitting a session. We explore the application of our technique to the recognition of typical scenarios for mechanical ventilation management.

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Author information

Authors and Affiliations

  1. HEUDIASYC URA CNRS 817, Université de Technologie de Compiègne, Compiègne, France

    Nicolas Ramaux & Dominique Fontaine

  2. INSERM Unité 296, Institut National de la Santé et de la Recherche Médicale, Créteil, France

    Michel Dojat

Authors
  1. Nicolas Ramaux
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  2. Dominique Fontaine
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  3. Michel Dojat
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Editor information

Elpida Keravnou Catherine Garbay Robert Baud Jeremy Wyatt

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© 1997 Springer-Verlag Berlin Heidelberg

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Ramaux, N., Fontaine, D., Dojat, M. (1997). Temporal scenario recognition for intelligent patient monitoring. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029466

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  • DOI: https://doi.org/10.1007/BFb0029466

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62709-8

  • Online ISBN: 978-3-540-68448-0

  • eBook Packages: Springer Book Archive

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