Multi-dimensional temporal abstraction and data mining of medical time series data: Trends and challenges
Christina Catley, Heidi Stratti, Carolyn McGregor · 2008
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.