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dc.contributor.authorBlanco, Xiomara
dc.contributor.authorRodríguez González, Sara 
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.contributor.authorZato Domínguez, Davinia Carolina 
dc.date.accessioned2017-09-06T09:14:02Z
dc.date.available2017-09-06T09:14:02Z
dc.date.issued2013
dc.identifier.citationDistributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing. Volumen 217, pp. 137-146.
dc.identifier.isbn978-3-319-00550-8 (Print) / 978-3-319-00551-5 (Online)
dc.identifier.issn2194-5357 (Print) / 2194-5365 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/134834
dc.description.abstractThe Case-Based Reasoning (CBR) is an appropriate methodology to apply in diagnosis and treatment. Research in CBR is growing and there are shortcomings, especially in the adaptation mechanism. In this paper, besides presenting a methodological review of the technology applied to the diagnostics and health sector published in recent years, a new proposal is presented to improve the adaptation stage. This proposal is focused on preparing the data to create association rules that help to reduce the number of cases and facilitate learning adaptation rules.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleCase-Based Reasoning Applied to Medical Diagnosis and Treatment
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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