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dc.contributor.authorStoean, Ruxandra
dc.date.accessioned2016-05-16T11:49:19Z
dc.date.available2016-05-16T11:49:19Z
dc.date.created2016
dc.date.issued2016-05-16
dc.identifier.urihttp://hdl.handle.net/10630/11407
dc.description.abstractComputational intelligent support for decision making is becoming increasingly popular and essential among medical professionals. Also, with the modern medical devices being capable to communicate with ICT, created models can easily find practical translation into software. Machine learning solutions for medicine range from the robust but opaque paradigms of support vector machines and neural networks to the also performant, yet more comprehensible, decision trees and rule-based models. So how can such different techniques be combined such that the professional obtains the whole spectrum of their particular advantages? The presented approaches have been conceived for various medical problems, while permanently bearing in mind the balance between good accuracy and understandable interpretation of the decision in order to truly establish a trustworthy ‘artificial’ second opinion for the medical expert.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andlaucía Tech.es_ES
dc.language.isospaes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectToma de decisioneses_ES
dc.subjectInteligencia computacionales_ES
dc.subject.otherDecision Making Supportes_ES
dc.subject.otherMachine learning,es_ES
dc.subject.otherComputacional Intelligence in Medecinees_ES
dc.titleCombined Machine Learning Techniques for Decision Making Support in Medicinees_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. de Telecomunicaciónes_ES
dc.relation.eventtitleConferencia Investigaciónes_ES
dc.relation.eventplaceETSI Telecomunicación-Málagaes_ES
dc.relation.eventdate12/06/2016es_ES
dc.cclicenseby-nc-ndes_ES


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