A Concise Ontology to Support Research on Complex, Multimodal Clinical Reasoning
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Authors
Rashid, Sabbir
McCusker, Jamie
Gruen, Daniel
Seneviratne, Oshani
McGuinness, Deborah L.
Issue Date
2023-05-22
Type
Article
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Abstract
When clinicians perform tasks involving clinical reasoning, such as the diagnosis or treatment of diabetes, multiple forms of reasoning, including deduction and abduction, are often employed. Ontologies designed to provide a foundation for clinical decision support systems have been encoded based on Clinical Practice Guidelines. Nevertheless, existing approaches solely allow deductive rules for clinical reasoning, with ontologies too large or complex to support tractable abductive reasoning. We follow existing guidelines and standards to design the Diabetes Pharmacology Ontology, a concise ontology – an ontology engineered by adhering to the Minimum Information to Reference an External Ontology Term principle and following an agile design approach. We claim that use cases that incorporate multiple forms of reasoning, such as those aimed at supporting both deduction and abduction, are better supported by concise, rather than complete and comprehensive, ontologies. We demonstrate how Personal Health Knowledge Graphs have been implemented using our ontology and evaluate the abductive capability of modules included with our ontology. We openly publish the resources that have resulted from this work, as listed below. This work demonstrates how multimodal semantic reasoning – deduction and abduction – can be used to emulate tasks involving clinical reasoning and thus has the potential to support practitioners with clinical decision-making.
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Full Citation
Rashid, S.M., McCusker, J., Gruen, D., Seneviratne, O., McGuinness, D.L. (2023). A Concise Ontology to Support Research on Complex, Multimodal Clinical Reasoning. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_23
Publisher
Springer, Cham