Enabling Trust in Clinical Decision Support Recommendations through Semantics
AuthorSeneviratne, Oshani; Das, Amar K.; Agu, Nkechinyere; Rashid, Sabbir M.; Chen, Ching-Hua; McCusker, Jamie P.; Hendler, Jim; McGuinness, Deborah
Full CitationSeneviratne, O., Das, A.K., Chari, S., Agu, N.N., Rashid, S.M., Chen, C.H., McCusker, J.P., Hendler, J.A. and McGuinness, D.L., 2019, October. Enabling Trust in Clinical Decision Support Recommendations through Semantics. In SeWeBMeDa@ ISWC (pp. 55-67).
MetadataShow full item record
AbstractIn an ideal world, the evidence presented in a clinical guideline would cover all aspects of patient care and would apply to all types of patients; however, in practice, this rarely is the case. Existing medical decision support systems are often simplistic, rule-based, and not easily adaptable to changing literature or medical guidelines. We are exploring ways that we can enable clinical decision support systems with Semantic Web technologies that have the potential to support representation and linking to details in the related items in the scientific literature, and that can quickly adapt to changing information from the guidelines. In this paper, we present the ontologies and our semantic web-based tools aimed at trustworthy clinical decision support in three distinct areas: guideline representation and reasoning, guideline provenance, and study cohort modeling.;
PublisherCEUR Workshop Proceedings (CEUR-WS.org)
The following license files are associated with this item: