Developing Bayesian networks from a dependency-layered ontology: A proof-of-concept in radiation oncology

Medical Physics
Alan M KaletMark H Phillips

Abstract

Bayesian networks (BNs) are graphical representations of probabilistic knowledge that offer normative reasoning under uncertainty and are well suited for use in medical domains. Traditional knowledge-based network development of BN topology requires that modeling experts establish relevant dependency links between domain concepts by searching and translating published literature, querying domain experts, or applying machine learning algorithms on data. For initial development these methods are time-intensive and this cost hinders the growth of BN applications in medical decision making. Further, this approach fails to utilize knowledge representation in medical fields to automate network development. Our research alleviates the challenges surrounding BN modeling in radiation oncology by leveraging an ontology based hub and spoke system for BN construction. We implement a hub and spoke system by developing (a) an ontology of knowledge in radiation oncology (the hub) which includes dependency semantics similar to BN relations and (b) a software tool that operates on ontological semantics using deductive reasoning to create BN topologies (the spokes). We demonstrate that network topologies built using the software are terminologic...Continue Reading

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Citations

Mar 31, 2019·Medical Physics·Samuel M H LukAlan M Kalet
Dec 12, 2020·International Journal of Production Research·Heng ZhangYung-Tsun Tina Lee

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