G-PROV: Provenance Management for Clinical Practice Guidelines

Authors
Agu, Nkechinyere
Keshan, Neha
Chari, Shruthi
Seneveratne, Oshani
Rashid, Sabbir
Das, Amar K.
McCusker, Jamie
McGuinness, Deborah L.
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Issue Date
2019-10
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Attribution-NonCommercial-NoDerivs 3.0 United States
Full Citation
Nkechinyere Agu, Neha Keshan, Shruthi Chari, Oshani Seneveratne, Sabbir M. Rashid, Amar K. Das, James P. McCusker and Deborah L. McGuinness. G-PROV: Provenance Management for Clinical Practice Guidelines. Semantic Web solutions for large-scale biomedical data analytics (SeWeBMeDA) Co-located with the International Semantic Web Conference, Auckland, NZ, October, 2019. *
Abstract
Providing provenance of treatment suggestions made by clinical decision support systems can enhance transparency and trust in these systems by healthcare practitioners. Provenance can aid in resolving ambiguity and conflicts between various guideline sources. We have developed a guideline provenance ontology, G-Prov, by extending existing provenance ontologies, to enable accurate encoding of the source of the reasoning rules that decision support systems rely on to generate diagnosis and treatment suggestions. Our ontology enables provenance representation at different granularity levels within guidelines. For instance, G-Prov can be used to annotate rules with citations found in evidence sentences as well as other sources of knowledge, such as figures and tables. Additionally, we have developed an application to show a range of use cases for our ontology. We demonstrate our work annotating recommendations in a CPG for Type-2 Diabetes and discuss how our approach could be used in various medical settings where CPGs are utilized.
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CEUR-WS
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