Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1646
DC FieldValueLanguage
dc.contributor.authorZupan, Blažen
dc.contributor.authorLavrač, Nadaen
dc.contributor.authorKeravnou-Papailiou, Elpida-
dc.date.accessioned2013-02-14T11:18:12Zen
dc.date.accessioned2013-05-17T05:22:13Z-
dc.date.accessioned2015-12-02T09:55:19Z-
dc.date.available2013-02-14T11:18:12Zen
dc.date.available2013-05-17T05:22:13Z-
dc.date.available2015-12-02T09:55:19Z-
dc.date.issued1999en
dc.identifier.citationArtificial intelligence in medicine, 1999, Volume 16, Issue 1, Pages 1-2en
dc.identifier.issn0933-3657en
dc.identifier.urihttps://hdl.handle.net/20.500.14279/1646-
dc.description.abstractIn his excellent article on ‘the adolescence of AI in Medicine’, Edward H. Shortliffe (AIM, 1993, 5:93-106) exposes three factors that may influence the successful integration of AI systems into patient-care settings: enhancement of training, international standards, and information infrastructure. Since 1993, information infrastructure has certainly advanced more than the other two factors. Nowadays, modern hospitals and health care institutions are rapidly advancing their information systems. What was previously an isolated data base or a laboratory information system is now integrated in a larger scale (departmental, hospital, or community-based) medical information systemen
dc.language.isoenen
dc.rights© 1999 Elsevier Science B.V. All rights reserveden
dc.subjectArtificial intelligenceen
dc.subjectDatabasesen
dc.subjectInformation systemsen
dc.subjectPatient--Careen
dc.subjectMedicineen
dc.titleData mining techniques and applications in medicineen
dc.typeArticleen
dc.affiliationUniversity of Cyprusen
dc.identifier.doihttp://dx.doi.org/10.1016/S0933-3657(98)00061-Xen
dc.dept.handle123456789/54en
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en-
item.fulltextNo Fulltext-
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