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Human-Computer interaction to learn scenarios from ICU multivariate time series

Thomas Guyet 1 Catherine Garbay 1 Michel Dojat 2, * 
* Corresponding author
TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
Abstract : Abstract. In the context of high dependent environments such as intensive care or operating room we would like to predict, as soon as possible, from past and present states, the evolution of the patient's condition. Based on the set of physiological dat, clinicians have difficulties to formalize typical high level scenes that are representative of specific patient's state sequences to recognize. On the other hand, signal processing algorithms are limited to low level pattern detection. We propose an interactive environment for an in-depth exploration by the clinician of data. We hypothesize that such human-computer collaboration could help with the definition of signatures representative of specific situations. To test our hypothesis, we have defined a multi-agent system (MAS) with the capacities of 1) segmenting, 2) classifying and 3) learning. These three tasks are continuously adapted based on interactions with the clinician. Preliminary results are presented to support our assumption.
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Submitted on : Wednesday, June 8, 2011 - 8:33:08 PM
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  • HAL Id : inserm-00519870, version 1


Thomas Guyet, Catherine Garbay, Michel Dojat. Human-Computer interaction to learn scenarios from ICU multivariate time series. Artificial Intelligence in Medicine. Proceedings of the European Conference on Artificial Intelligence in Medicine AIME'05, Jul 2005, Aberdeen- Scotland, United Kingdom. pp.424-428. ⟨inserm-00519870⟩



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