Mining temporal patterns from health care data

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. © 2002 Springer-Verlag Berlin Heidelberg.