Abstract
This paper presents a method to extract on line successive temporal sequences from high frequency data monitored in ICU. Successive temporal sequences are expressions such as: “the systolic blood pressure is steady at 120mmHg from time t 0 until time t 1; it is increasing from 120 mmHg to 160mmHg from time t 1 to time t 2 ...”. The method uses a segmentation algorithm that was developed previously and a classification of the segments into temporal patterns. It has seven tuning parameters that are rather easy to tune because they have a physical meaning. The results obtained on simulated data are quite satisfactory. Sequences extracted from real biological data recorded during 14 hours from different patients received the approbation of two clinicians. These temporal sequences can help the health care personnel to take decisions in alarm situations, or can be used as inputs to intelligent alarm systems using inferences on the data.
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Charbonnier, S. (2003). On-Line Extraction of Successive Temporal Sequences from ICU High-Frequency Data for Decision Support Information. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_1
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DOI: https://doi.org/10.1007/978-3-540-39907-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20129-8
Online ISBN: 978-3-540-39907-0
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