Article

Towards symbolization using data-driven extraction of local trends for ICU monitoring.

Laboratoire d'Automatique et Informatique Industrielle de Lille, Bâtiment P2 Cité Scientifique, Villeneuve d'Ascq, France.
Artificial Intelligence in Medicine (Impact Factor: 1.36). 08/2000; 19(3):203-23. DOI: 10.1016/S0933-3657(00)00046-4
Source: PubMed

ABSTRACT We propose a methodology for the extraction of local trends from a stream of data. It has been designed to suit the needs of interpretation-oriented visualization and symbolization from ICU monitoring data. After giving implementation details for efficient computation of local trends, we propose the use of a characteristic analysis span for each variable. This characteristic span is obtained from a set of criteria that we compare and evaluate in regard of analysis of ICU monitoring data gathered within the Aiddaig project. The processing results in a rich visual representation and a framework for the local symbolization of the data stream based on its dynamics.

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    • "A patient record is a long, high frequency, multivariate time series data. Classical monitoring improvement techniques are focused on algorithms at a numerical level (Tsien et al., 2000; Calvelo et al., 2000). On the contrary, we propose that symbolic abstraction could bring useful information to data, providing a better understanding of these still poorly formalized data but also, and more deeply, a new approach for monitoring based on dynamic anomaly detection. "
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