Analysing Time Series Medical Data-sets

Sleeman, D and Luo, Z and Christie, G and Coghill, G (2004) Analysing Time Series Medical Data-sets . Technical Report, Proceedings of Knowledge Based Systems & Services for Health Care, Bonn: May 2004, p 1-4..

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At the 1999 AIME Conference [1] we reported a decision tree study on a subset of data, including time series physiological data, admissions characteristics, and outcome after traumatic head injury. That study analysed total duration for which patients had, for example, raised Intracranial Pressure, but it did not consider temporal relationships between various physiological and clinical events. In this study, we have addressed that issue in a number of ways. Firstly, by using a workbench, AAB, to display the Real Time data-set and asking clinicians to make predictions of expected outcome based on complete physiological and clinical data. Secondly, repeating the exercise with a reduced (more compact) representation for the physiological data. Thirdly, patterns were generated, including “adjacent” physiological parameters and clinicians were asked if they are likely/very unlikely to cause a particular major physiological event or outcome. Finally, we implemented a module to test patterns of the form: IF X happens then Y will happen between T1-T2 against patient time-series data. Results of all these studies have so far not been conclusive [2]; it has been suggested that the brain is currently not very well understood physiologically, and that a similar set of analyses should be applied to a simpler organ. Given that significant amounts of data are now available for patients undergoing dialysis, we have chosen to do an analogous study in this area; also the physiology of the renal system is much better understood. We have outlined some additional studies we plan to undertake using data-mining, theory refinement and knowledge base refinement approaches.

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