Abstract
This paper describes temporal data mining techniques for extracting information from temporal health records consisting of a time series of elderly diabetic patients’ tests. We propose a data mining procedure to analyse these time sequences in three steps to identify patterns from any longitudinal data set. The first step is a structure-based search using wavelets to find pattern structures. The second step employs a value-based search over the discovered patterns using the statistical distribution of data values. The third step combines the results from the first two steps to form a hybrid model. The hybrid model has the expressive power of both wavelet analysis and the statistical distribution of the values. Global patterns are therefore identified.
Keywords
- Test Group
- Pattern Discovery
- Elderly Diabetic Patient
- Health Care Data
- Frequency Distribution Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Research supported in part by the Australian Research Council (ARC).
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© 2002 Springer-Verlag Berlin Heidelberg
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Lin, W., Orgun, M.A., Williams, G.J. (2002). Mining Temporal Patterns from Health Care Data* . In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2002. Lecture Notes in Computer Science, vol 2454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46145-0_22
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DOI: https://doi.org/10.1007/3-540-46145-0_22
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