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Knowledge construction from time series data using a collaborative exploration system.

Thomas Guyet 1 Catherine Garbay 2 Michel Dojat 3, * 
* Corresponding author
1 TIMC-IMAG-PRETA - Physiologie cardio-Respiratoire Expérimentale Théorique et Appliquée
TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
Abstract : This paper deals with the exploration of biomedical multivariate time series to construct typical parameter evolution or scenarios. This task is known to be difficult: the temporal and multivariate nature of the data at hand and the context-sensitive aspect of data interpretation hamper the formulation of a priori knowledge about the kind of patterns that can be detected as well as their interrelations. This paper proposes a new way to tackle this problem based on a human-computer collaborative approach involving specific annotations. Three grounding principles, namely autonomy, adaptability and emergence, support the co-construction of successive abstraction levels for data interpretation. An agent-based design is proposed to support these principles. Preliminary results in a clinical context are presented to support our proposal. A comparison with two well-known time series exploration tools is furthermore performed.
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Thomas Guyet, Catherine Garbay, Michel Dojat. Knowledge construction from time series data using a collaborative exploration system.. Journal of Biomedical Informatics, Elsevier, 2007, 40 (6), pp.672-87. ⟨10.1016/j.jbi.2007.09.006⟩. ⟨inserm-00381739⟩



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