Using Bayesian Networks for Discovering Temporal-State Transition Patterns in Hemodialysis

In this paper, we adopt Bayesian networks for discovering temporal-state transition patterns in the Hemodialysis process. Bayesian network is a graphical model that encodes probabilistic relationships among variables, and easily incorporatesnew instances to maintain rules up to date. 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 Hemodialysis clinical pathway patterns can be used for predicting possible paths for an admitted patient, and to help medical professionals to react to exceptions during the Hemodialysis process. The discovery of clinical pathway patterns enables reciprocal learning cycle for medical organizational knowledge management.