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dc.contributor.authorSan Miguel Carrasco, Rafael (1)
dc.date2016-03
dc.date.accessioned2017-10-23T16:13:43Z
dc.date.available2017-10-23T16:13:43Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/5817
dc.description.abstractGeriatrics Medicine constitutes a clinical research field in which data analytics, particularly predictive modeling, can deliver compelling, reliable and long-lasting benefits, as well as non-intuitive clinical insights and net new knowledge. The research work described in this paper leverages predictive modeling to uncover new insights related to adverse reaction to drugs in elderly patients. The differentiation factor that sets this research exercise apart from traditional clinical research is the fact that it was not designed by formulating a particular hypothesis to be validated. Instead, it was data-centric, with data being mined to discover relationships or correlations among variables. Regression techniques were systematically applied to data through multiple iterations and under different configurations. The obtained results after the process was completed are explained and discussed next.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligencees_ES
dc.relation.ispartofseries;vol. 3, nº 6
dc.relation.urihttp://www.ijimai.org/journal/node/939es_ES
dc.rightsopenAccesses_ES
dc.subjectgeriatricses_ES
dc.subjectmedicinees_ES
dc.subjectdata analyticses_ES
dc.subjectstatistical analysises_ES
dc.subjectpredictive modelinges_ES
dc.subjectknowledge managementes_ES
dc.subjectadverse reactionses_ES
dc.subjectdrugses_ES
dc.subjectEmerginges_ES
dc.titleDetection of Adverse Reaction to Drugs in Elderly Patients through Predictive Modelinges_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttp://dx.doi.org/10.9781/ijimai.2016.368


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