Abstract
Clinical Practice Guidelines (CPGs) play an important role in improving quality of care and patient outcomes. Although several machine-readable representations of practice guidelines have been implemented with semantic web technologies, there is no implementation to represent uncertainty in activity graphs in clinical practice guidelines. In this paper, we explore a Bayesian Network(BN) approach for representing the uncertainty in CPGs based on ontologies. Using this representation, we can evaluate the effect of an activity on the whole clinical process, which can help doctors judge the risk of uncertainty for other activities when making 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.
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Zheng, HT., Kang, BY., Kim, HG. (2008). An Ontology-Based Bayesian Network Approach for Representing Uncertainty in Clinical Practice Guidelines. In: da Costa, P.C.G., et al. Uncertainty Reasoning for the Semantic Web I. URSW URSW URSW 2006 2007 2005. Lecture Notes in Computer Science(), vol 5327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89765-1_10
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DOI: https://doi.org/10.1007/978-3-540-89765-1_10
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