Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/32070

TitleDevelopment and implementation of clinical guidelines : an artificial intelligence perspective
Author(s)Oliveira, Tiago José Martins
Novais, Paulo
Neves, José
KeywordsComputer-interpretable guidelines
Ontologies
Decision support
Quality of information
Issue date2014
PublisherSpringer
JournalArtificial Intelligence Review
Abstract(s)Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.
TypeArticle
URIhttp://hdl.handle.net/1822/32070
DOI10.1007/s10462-013-9402-2
ISSN0269-2821
1573-7462
Publisher versionhttp://link.springer.com/article/10.1007/s10462-013-9402-2
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CCTC - Artigos em revistas internacionais

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