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

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...

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Bibliographic Details
Published in:Artificial intelligence in medicine 2000-07, Vol.19 (3), p.203-223
Main Authors: Calvelo, D, Chambrin, M.-C, Pomorski, D, Ravaux, P
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer applications
Computer Science
Data acquisition
Data reduction
Hospital data processing
Hospitals
Human health and pathology
Humans
Intensive Care Units
Life Sciences
Machine Learning
Medicine
Patient monitoring
Regression
Regression analysis
Signal and Image Processing
Signal Processing, Computer-Assisted
Span
Statistics
Symbolism
Symbolization
Trend
Visualization
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Summary: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.
ISSN:0933-3657
1873-2860
DOI:10.1016/S0933-3657(00)00046-4