ADA: An online trend pattern detection system

Zhang, Qing, Pang, Chaoyi, Xie, Qing, Mcbride, Simon, Hansen, David and Zhang, Yanchun (2010). ADA: An online trend pattern detection system. In: Gang Kou, Yi Peng, Franz I.S. Ko, Yen-Wei Chen and Tomoko Tateyama, Proceedings of the The 2nd International Conference on Software Engineering and Data Mining. SEDM 2010, Chengdu, China, (293-297). 23-25 June, 2010.

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Name Description MIMEType Size Downloads
Author Zhang, Qing
Pang, Chaoyi
Xie, Qing
Mcbride, Simon
Hansen, David
Zhang, Yanchun
Title of paper ADA: An online trend pattern detection system
Conference Paper Type Fully Published Paper
Conference name SEDM 2010
Conference location Chengdu, China
Conference dates 23-25 June, 2010
Proceedings title Proceedings of the The 2nd International Conference on Software Engineering and Data Mining
Editor Gang Kou
Yi Peng
Franz I.S. Ko
Yen-Wei Chen
Tomoko Tateyama
Place published Piscatawa, NJ, U.S.A.
Publisher IEEE
Publication date 2010
Issue number Article number 5542890
ISBN 9781424473243
Start page 293
End page 297
Total pages 5
Collection year 2011
Language eng
Abstract/Summary Pattern recognition has been used extensively in medical information retrieval and data analyses. Specifically, it involves pattern classification, indexing, clustering, anomaly detection and rule detection. Among various patterns, trend is a simple yet powerful pattern that can be associated with many complex clinical symptoms. Detecting adverse clinical trend is thus an important proactive approach to critical clinical situation managements. In this paper, we propose an online trend pattern detection system, the Anaesthetic Data Analyser (ADA), as a platform to monitor trend patterns of physiological data collected during anaesthesia. ADA differentiates from current approaches by looking at trends rather than a single data value against a preset threshold. Our online trend pattern detection and trend query processing algorithms also make ADA support real time trend monitoring efficiently. Experiments on physiological data collected from patients demonstrate the efficiency and effectiveness of the ADA system and our algorithms.
Keyword Anaesthetic Data
Pattern Recognition
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes session B1

Document type: Conference Paper
Sub-type: Fully Published Paper
Collections: Non HERDC
School of Information Technology and Electrical Engineering Publications
 
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Created: Fri, 25 Mar 2011, 10:39:52 EST by Mr Felix Xie on behalf of School of Information Technol and Elec Engineering