MCSTAY, Gavin and Krichel, Thomas (2021) Poster abstracts from fourth annual public meeting: Mobilizing computable biomedical knowledge (MCBK 2021). Learning Health Systems, 6 (1). e10300. ISSN 2379-6146
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Abstract or description
Modern cognitive support applications are complex systems that com-bine background (bio)medical knowledge with knowledge about physio-logical processes, decision-making criteria, evidence-based guidelines,clinical workflows, human-computer interaction protocols, and seman-tics of data sources, including electronic health records. For each cate-gory of knowledge artifacts, the knowledge can be curated, distributed,and executed using different industry standards—usually published byorganizations such as OMG, HL7, OASIS, and the W3C—leveragingcommodity tools for representation and reasoning purposes. To managethe inherent complexity, we propose an architectural approach basedon the notion of institution1first applied to ontology languages. Eachartifact is categorized in terms of its knowledge content, described usingmetadata about its representation (language/profile, terminology, seriali-zation, encoding), annotated semantically, and related using the notionsof composition, derivation, and dependency. This architecture is anOMG standard itself (API4KP2) and adheres to the MCBK principles3ofmetadata fairness and trust. As a demonstration, we present the“MayoExpert Advisor”use case: an application framework that involves 20+types of knowledge artifacts and uses 15+knowledge representationstandards. This poster presentation is particularly relevant to knowledgeengineers trying to build architectures that support different types ofinteroperable computable biomedical knowledge artifacts.
Item Type: | Article |
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Faculty: | School of Law, Policing and Forensics > Sociology, Criminology and Terrorism |
Event Title: | Mobilizing Computable Biological Knowledge 2021 Annual Meeting |
Event Location: | Online Conferance |
Event Dates: | July 20–July 21, 2021 |
Depositing User: | Gavin MCSTAY |
Date Deposited: | 16 Feb 2022 11:54 |
Last Modified: | 24 Feb 2023 14:03 |
URI: | https://eprints.staffs.ac.uk/id/eprint/7171 |