Title page for etd-0805102-125552


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URN etd-0805102-125552
Author Woei-Ru Wang
Author's Email Address m8942617@student.nsysu.edu.tw
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Department Information Management
Year 2001
Semester 2
Degree Master
Type of Document
Language English
Title Constructing Bayesian Networks with Sequential Patterns for Hemodialysis
Date of Defense 2002-07-19
Page Count 56
Keyword
  • knowledge management
  • data mining
  • Bayesian network
  • Hemodialysis
  • clustering
  • sequential pattern
  • Abstract 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.
    Advisory Committee
  • San-Yih Hwang - chair
  • Chih-Ping Wei - co-chair
  • Fu-Ren Lin - advisor
  • Files
  • etd-0805102-125552.pdf
  • indicate in-campus access immediately and off_campus access in a year
    Date of Submission 2002-08-05

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