| Summary: | 碩士 === 國立中山大學 === 資訊管理學系研究所 === 90 ===
In this thesis, I introduce a multivariate discretization algorithm to discretize the continuous variables of clinical pathways of Hemodialysis and use the clustering algorithm to shift time stamps to reduce the number of nodes of Bayesian networks. The generalized sequential patterns algorithm is used to find the possible patterns, which have far-reaching effect on the next nodes of the Bayesian networks of Hemodialysis. Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest, and easily incorporates with new instances to maintain rules up to date. Bayesian networks are used to represent knowledge of frequent state transitions in medical logs. Bayesian networks and sequential patterns algorithms can only handle discrete or categorical data. Therefore, we have to discretize the continuous variables with suitable technique to generalize the node, and shift the time stamps of nodes to reduce the variations in time. With these generalizations, we improve the problem of over-fitting of the Bayesian networks of Hemodialysis. We expect the discovered patterns can give more information to medical professionals and help them to build the reciprocal cycle of knowledge management of Hemodialysis.
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