An Ontology-based Bayesian Network Approach for Representing Uncertainty in Clinical Practice Guidelines

Clinical Practice Guidelines (CPGs) play an important role in improving the quality of care and patient outcomes. Although several machine-readable representations of practice guidelines implemented with semantic web technologies have been presented, there is no implementation to represent uncertainty with respect to activity graphs in clinical practice guidelines. In this paper, we are exploring a Bayesian Network(BN) approach for representing the uncertainty in CPGs based on ontologies. Based on the representation of uncertainty in CPGs, when an activity occurs, we can evaluate its effect on the whole clinical process, which, in turn, can help doctors judge the risk of uncertainty for other activities, and make a decision. A variable elimination algorithm is applied to implement the BN inference and a validation of an aspirin therapy scenario for diabetic patients is proposed.

• Type: Conference
• Year: 2007
• Where: SEMWEB
• Authors: Hai-Tao Zheng, Bo-Yeong Kang, Hong-Gee Kim
• Keywords: Clinical Practice Guidelines, Semantic Web Technologies, SEMWEB 2007, Variable Elimination Algorithm