Multi-dimensional temporal abstraction and data mining of medical time series data: trends and challenges.
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.
Catley
Christina
C
Faculty of Health Sciences and Faculty of Business and IT, University of Ontario Institute of Technology, Oshawa, Canada. ccatley@ieee.org
Stratti
Heidi
H
McGregor
Carolyn
C
D000465:Algorithms; D001185:Artificial Intelligence; D003625:Data Collection; D003661:Decision Support Techniques; D005260:Female; D005865:Gestational Age; D006801:Humans; D016347:Medical Records Systems, Computerized / Q000295:instrumentation*; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011247:Pregnancy; D047928:Premature Birth; D015203:Reproducibility of Results; D013813:Therapy, Computer-Assisted; D013997:Time Factors