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Title: Temporal diagnostic reasoning based on time-objects
Authors: Keravnou-Papailiou, Elpida
Keywords: Computer science;Artificial intelligence;Medicine;Expert systems (Computer science);Knowledge representation;Ontology
Issue Date: 1996
Publisher: Elsevier
Source: Artificial intelligence in medicine, 1996, Volume 8, Issue 3, Pages 235–265
Abstract: Time 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-objects
ISSN: 0933-3657
Rights: © 1996 Published by Elsevier B.V.
Type: Article
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