Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1707
DC FieldValueLanguage
dc.contributor.authorKeravnou-Papailiou, Elpida-
dc.contributor.otherΚεραυνού-Παπαηλιού, Ελπίδα-
dc.date.accessioned2013-02-14T13:14:56Zen
dc.date.accessioned2013-05-17T05:22:20Z-
dc.date.accessioned2015-12-02T09:59:55Z-
dc.date.available2013-02-14T13:14:56Zen
dc.date.available2013-05-17T05:22:20Z-
dc.date.available2015-12-02T09:59:55Z-
dc.date.issued1996en
dc.identifier.citationArtificial intelligence in medicine, 1996, Volume 8, Issue 3, Pages 235–265en
dc.identifier.issn0933-3657en
dc.identifier.urihttps://hdl.handle.net/20.500.14279/1707-
dc.description.abstractTime is essential in diagnostic problem-solving. However, as with other commonsense tasks, time representation and reasoning is not a trivial undertaking. This probably explains why time has either been ignored or implicitly represented and used in the majority of diagnostic systems, medical or otherwise. Durations, temporal uncertainty and multiple temporal granularities are necessary requirements for medical problem-solving. Most general theories of time proposed in the literature do not address all these requirements, and some do not address any. The paper discusses time representation and reasoning in medical diagnostic problem-solving, building from a generic temporal ontology which covers the above temporal requirements. Much of what is discussed, however, is applicable to non-medical domains as well. It is argued that the diagnostic concepts (patient data, disorders, therapeutic-actions) are naturally modelled as time-objects. The resulting representation treats time as an integral dimension to these concepts, with special status. Time-object-based representations for generic hypotheses (disorders, actions) are discussed and illustrated; in the case of disorders the representation covers both an associational model and a causal-associational model. A central function of diagnostic problem-solving is deciding the compatibility of hypotheses with regard to a patient model. In this respect the paper discusses temporal and contextual screening of triggered hypotheses as well as accountings and conflicts between time-objectsen
dc.language.isoenen
dc.rights© 1996 Published by Elsevier B.V.en
dc.subjectComputer scienceen
dc.subjectArtificial intelligenceen
dc.subjectMedicineen
dc.subjectExpert systems (Computer science)en
dc.subjectKnowledge representationen
dc.subjectOntologyen
dc.titleTemporal diagnostic reasoning based on time-objectsen
dc.typeArticleen
dc.affiliationUniversity of Cyprusen
dc.identifier.doihttp://dx.doi.org/10.1016/0933-3657(95)00035-6en
dc.dept.handle123456789/54en
item.openairetypearticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.grantfulltextnone-
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