Enabling Trust in Clinical Decision Support Recommendations through Semantics

Authors
Seneviratne, Oshani
Das, Amar K.
Chari, Shruthi
Agu, Nkechinyere
Rashid, Sabbir
Chen, Ching-Hua
McCusker, Jamie
Hendler, James A.
McGuinness, Deborah L.
ORCID
Loading...
Thumbnail Image
Other Contributors
Issue Date
2019
Keywords
Degree
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
Full Citation
Seneviratne, 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).
Abstract
In 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.
Description
Department
Publisher
CEUR Workshop Proceedings (CEUR-WS.org)
Relationships
Access