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. © 2007 Elsevier Inc. All rights reserved.
CITATION STYLE
Guyet, T., Garbay, C., & Dojat, M. (2007). Knowledge construction from time series data using a collaborative exploration system. Journal of Biomedical Informatics, 40(6), 672–687. https://doi.org/10.1016/j.jbi.2007.09.006
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