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Classification of primary progressive aphasia: do unsupervised data mining methods support a logopenic variant?

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dc.contributor.authorMaruta, Carolina
dc.contributor.authorPereira, Telma
dc.contributor.authorMadeira, Sara C.
dc.contributor.authorDe Mendonça, Alexandre
dc.contributor.authorGuerreiro, Manuela
dc.date.accessioned2022-01-27T15:43:15Z
dc.date.available2022-01-27T15:43:15Z
dc.date.issued2015
dc.description© 2015 Informa Healthcarept_PT
dc.description.abstractOur objective was to test whether data mining techniques, through an unsupervised learning approach, support the three-group diagnostic model of primary progressive aphasia (PPA) versus the existence of two main/classic groups. A series of 155 PPA patients observed in a clinical setting and subjected to at least one neuropsychological/language assessment was studied. Several demographic, clinical and neuropsychological attributes, grouped in distinct sets, were introduced in unsupervised learning methods (Expectation Maximization, K-Means, X-Means, Hierarchical Clustering and Consensus Clustering). Results demonstrated that unsupervised learning methods revealed two main groups consistently obtained throughout all the analyses (with different algorithms and different set of attributes). One group included most of the agrammatic/non-fluent and some logopenic cases while the other was mainly composed of semantic and logopenic cases. Clustering the patients in a larger number of groups (k > 2) revealed some clusters composed mostly of non-fluent or of semantic cases. However, we could not evidence any group chiefly composed of logopenic cases. In conclusion, unsupervised data mining approaches do not support a clear distinction of logopenic PPA as a separate variant.pt_PT
dc.description.sponsorshipCM and TP are supported by Fundação para a Ciência e Tecnologia (FCT) PhD Fellowships (SFRH/BD/75710/2011 and SFRH/BD/95846/2013, respectively). AdM and MG also receive funding from FCT. TP and SM receive funding from NEURO- CLINOMICS (PTDC/EIA/111239/2009) and UID/CEC/50021/2013, also funded by FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAmyotroph Lateral Scler Frontotemporal Degener. 2015 Jun;16(3-4):147-59pt_PT
dc.identifier.doi10.3109/21678421.2015.1026266pt_PT
dc.identifier.eissn2167-9223
dc.identifier.issn2167-8421
dc.identifier.urihttp://hdl.handle.net/10451/51023
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherTaylor & Francispt_PT
dc.relationPTDC/EIA/111239/2009pt_PT
dc.relationA DATA MINING APPROACH TO STUDY DISEASE PRESENTATION AND PROGRESSION PATTERNS IN PPA AND MCI.
dc.relation.publisherversionhttps://www.tandfonline.com/journals/iafd20pt_PT
dc.subjectPrimary progressive aphasiapt_PT
dc.subjectData miningpt_PT
dc.subjectLogopenic variant (lvPPA)pt_PT
dc.subjectNon-fluent variant (nfvPPA)pt_PT
dc.subjectSemantic variant (svPPA)pt_PT
dc.titleClassification of primary progressive aphasia: do unsupervised data mining methods support a logopenic variant?pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberSFRH/BD/75710/2011
oaire.awardNumberSFRH/BD/95846/2013
oaire.awardNumberUID/CEC/50021/2013
oaire.awardTitleA DATA MINING APPROACH TO STUDY DISEASE PRESENTATION AND PROGRESSION PATTERNS IN PPA AND MCI.
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBD%2F75710%2F2011/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/SFRH%2FBD%2F95846%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F50021%2F2013/PT
oaire.citation.endPage159pt_PT
oaire.citation.issue3-4pt_PT
oaire.citation.startPage147pt_PT
oaire.citation.titleAmyotrophic Lateral Sclerosis and Frontotemporal Degenerationpt_PT
oaire.citation.volume16pt_PT
oaire.fundingStreamSFRH
oaire.fundingStreamOE
oaire.fundingStream5876
person.familyNameMaruta
person.familyNamePereira
person.familyNameMadeira
person.familyNamede Mendonça
person.familyNameGuerreiro
person.givenNameCarolina
person.givenNameTelma
person.givenNameSara
person.givenNameAlexandre
person.givenNameManuela
person.identifier.ciencia-id4B1D-5F59-6221
person.identifier.ciencia-idAF12-AA0D-0C7B
person.identifier.ciencia-id1615-41B4-0848
person.identifier.orcid0000-0003-3359-379X
person.identifier.orcid0000-0002-6780-4340
person.identifier.orcid0000-0002-1459-8096
person.identifier.orcid0000-0002-0488-1453
person.identifier.orcid0000-0002-1948-1516
person.identifier.ridC-5494-2008
person.identifier.scopus-author-id35327513800
person.identifier.scopus-author-id6602138051
person.identifier.scopus-author-id7003320823
person.identifier.scopus-author-id7005985367
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
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