Using Bayesian Networks for Discovering Temporal-State Transitions in Hemodialysis

• Main Authors: Chih-Hung Chiu, 邱志宏
• Other Authors: Fu-Ren Lin
• Format: Others
• Language: en_US
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/30404620710298181134

碩士 === 國立中山大學 === 資訊管理學系研究所 === 88 === In this thesis, we discover knowledge from workflow logs with temporal-state transitions in the form of Bayesian networks. 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. The Bayesian networks can predict, communicate, train, and offer more alternatives to make better decisions. We demonstrate the proposed method in representing the causal relationships between medical treatments and transitions of patient’s physiological states in the Hemodialysis process. The discovery of clinical pathway patterns of Hemodialysis can be used for predicting possible paths for an admitted patient, and facilitating medical professionals to control the Hemodialysis machines during the Hemodialysis process. The reciprocal knowledge management can be extended from the results in future research.