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A Human-Machine Cooperative Approach for Time Series Data Interpretation

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Artificial Intelligence in Medicine (AIME 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4594))

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Abstract

This paper deals with the interpretation of biomedical multivariate time series for extracting typical scenarios. This task is known to be difficult, due to the temporal nature of the data at hand, and to the context-sensitive aspect of data interpretation, which hamper the formulation of a priori knowledge about the kind of patterns to detect and their interrelations. A new way to tackle this problem is proposed, based on a collaborative approach between a human and a machine by means of specific annotations. Two grounding principles, namely autonomy and knowledge discovery, support the co-construction of successive abstraction levels for data interpretation. A multi-agent system is proposed to implement effectively these two principles. Respiratory time series data (Flow, Paw) have been explored with our system for patient/ventilator asynchronies characterization studies.

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Author information

Authors and Affiliations

  1. TIMC Laboratory, Domaine de la Merci F-38706 La Tronche, France

    Thomas Guyet

  2. CNRS/LIG Laboratoire d’Informatique de Grenoble, France

    Catherine Garbay

  3. UMR-S 836 Inserm/UJF/CEA “Institut des Neurosciences”, Grenoble, France

    Michel Dojat

Authors
  1. Thomas Guyet
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  2. Catherine Garbay
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  3. Michel Dojat
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Editor information

Riccardo Bellazzi Ameen Abu-Hanna Jim Hunter

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© 2007 Springer-Verlag Berlin Heidelberg

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Guyet, T., Garbay, C., Dojat, M. (2007). A Human-Machine Cooperative Approach for Time Series Data Interpretation. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_1

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  • DOI: https://doi.org/10.1007/978-3-540-73599-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73598-4

  • Online ISBN: 978-3-540-73599-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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