Multilevels Hidden Markov Models For Temporal Data Mining

This paper describes new temporal data mining techniques for extracting information from temporal health records consisting of time series of diabetic patients’ treatments. In this new method, there are three steps for analyzing patterns from a longitudinal data set. The first step, a structural-based pattern search, to find qualitative patterns (or, structural patterns). The second step performs a value-based search to find quantitative patterns. In the third step we combine results from the first two steps to form new model. The hidden Markov model has the expressive power of both qualitative analysis and data quantitative analysis. The global patterns can therefore be identified from a DTS set.