A framework for temporal abstractive multidimensional data mining

Abstract

In the industrialised world, premature birth has been recognised as one of the most significant perinatal health issues (Kramer, Platt et al. 1998). In Australia 8.1% of babies are born before 37 weeks gestation (Laws, Abeywardana et al. 2007). Premature babies often have prolonged stays in Neonatal Intensive Care Units (NICUs) and can suffer from a number of different conditions during their stay. Some of these conditions have been shown to exhibit certain variations in their physiological parameters that can indicate the onset of such conditions, before it can be detected by other means. Medical monitoring equipment produces large masses of data, which makes analysing this data manually impossible. Adding to the complexity of the large datasets is the nature of physiological monitoring data - the data is multidimensional, where it is not only changes in individual dimensions that are significant, but sometimes simultaneous changes in several dimensions. As the time-series produced by the monitoring equipment is temporal, there is a need for clinical research frameworks that enables both the dimensionality and temporal behaviour to be preserved during data mining. The aim of this research is to extend previous research that proposed a framework to support analysis and trend detection in historical data from Neonatal Intensive Care Unit (NICU) patients. The extensions contribute to fundamental data mining framework research through the integration of temporal abstraction and support of null hypothesis testing within the data mining processes. The application of this new data mining approach is the analysis of level shifts and trends in historical temporal data and to cross correlate data mining findings across multiple data streams for multiple neonatal intensive care patients in an attempt to discover new hypotheses indicative of the onset of some condition. These hypotheses can then be evaluated and defined as rules to be applied in the monitoring of neonates in real-time to enable early detection of possible onset of conditions. This can assist in faster decision making which in turn may avoid conditions developing into serious problems where treatment may be futile. This research employs a constructive research method. In this research, the problem is the inability of current data mining frameworks to completely support clinical research in multidimensional temporal data. This research has resulted in the design of a temporal abstraction multidimensional data mining (TAMDDM) framework suitable for clinical research in multidimensional temporal time series data. The framework is demonstrated through a case study with neonatal intensive care monitoring data.

Date of Award	2008
Original language	English