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Multi-dimensional temporal abstraction and data mining of medical time series data: Trends and challenges

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3 Author(s)
Catley, C. ; Faculty of Health Sciences and Faculty of Business and IT at the University of Ontario Institute of Technology, Oshawa, Canada ; Stratti, Heidi ; McGregor, C.

This paper presents emerging trends in the area of temporal abstraction and data mining, as applied to multi-dimensional data. The clinical context is that of Neonatal Intensive Care, an acute care environment distinguished by multi-dimensional and high-frequency data. Six key trends are identified and classified into the following categories: (1) data; (2) results; (3) integration; and (4) knowledge base. These trends form the basis of next-generation knowledge discovery in data systems, which must address challenges associated with supporting multi-dimensional and real-world clinical data, as well as null hypothesis testing. Architectural drivers for frameworks that support data mining and temporal abstraction include: process-level integration (i.e. workflow order); synthesized knowledge bases for temporal abstraction which combine knowledge derived from both data mining and domain experts; and system-level integration.

Published in:

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

Date of Conference:

20-25 Aug. 2008