A Framework for Temporal Abstractive Multidimensional Data Mining

ion (TA) across multiple parameters for multiple patients to enable mining of multi-dimensional temporal data 3. The TAMDDM framework can be applied in a neonatal context 4. The TAMDDM framework can support null hypothesis testing 5. The hypotheses generated by the framework can be used by a real-time event stream processor analysing the current condition of babies in a Neonatal Intensive Care Unit (NICU) 1.4 Contribution to knowledge The areas of research contribution to knowledge resulting from this thesis are: • Extensions to a multi agent framework previously designed for analysing time series data, to facilitate temporal abstraction and realignment of these abstractions (as presented in chapter 5) • Enable incorporation of the extended CRISP-DM model within the multi agent framework (as presented in chapter 5). • Design of a framework to enable temporally abstractive multi dimensional data mining (as presented in chapter 5) • Enhancement of the interaction between clinical research and clinical management by generating a framework for clinical research which can produce hypotheses that will feed into intelligent monitoring systems used in clinical management. The clinical research framework uses as input data from the various monitoring equipment used in clinical management processes (as presented in chapter 5 and chapter 6)