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Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

2013-03Journal article Open access
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dc.contributor.authorPereira, Telma
dc.contributor.authorLemos, Luis
dc.contributor.authorCardoso, Sandra
dc.contributor.authorSilva, Dina
dc.contributor.authorRodrigues, Ana
dc.contributor.authorSantana, Isabel
dc.contributor.authorde Mendonca, Alexandre
dc.contributor.authorGuerreiro, Manuela
dc.contributor.authorMadeira, Sara C.
dc.date.accessioned2018-12-07T14:53:38Z
dc.date.available2018-12-07T14:53:38Z
dc.date.issued2013-03
dc.description.abstractBackground: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.
dc.description.sponsorshipFCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013]
dc.identifier.doi10.1186/s12911-017-0497-2
dc.identifier.issn1472-6947
dc.identifier.urihttp://hdl.handle.net/10400.1/11608
dc.language.isoeng
dc.peerreviewedyes
dc.publisherBiomed Central Ltd
dc.relationNEUROCLINOMICS2 - Unravelling Prognostic Markers in NEUROdegenerative diseases through CLINical and OMICS data integration
dc.relationA DATA MINING APPROACH TO STUDY DISEASE PRESENTATION AND PROGRESSION PATTERNS IN PPA AND MCI.
dc.subjectAlzheimers Disease
dc.subjectConversion
dc.subjectDiagnosis
dc.subjectRecommendations
dc.subjectClassification
dc.subjectCriteria
dc.subjectTests
dc.subjectRates
dc.subjectMci
dc.titlePredicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleNEUROCLINOMICS2 - Unravelling Prognostic Markers in NEUROdegenerative diseases through CLINical and OMICS data integration
oaire.awardTitleA DATA MINING APPROACH TO STUDY DISEASE PRESENTATION AND PROGRESSION PATTERNS IN PPA AND MCI.
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEEI-SII%2F1937%2F2014/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.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F00408%2F2013/PT
oaire.citation.startPage38
oaire.citation.titleBmc Medical Informatics and Decision Making
oaire.citation.volume4
oaire.fundingStream3599-PPCDT
oaire.fundingStreamOE
oaire.fundingStream5876
oaire.fundingStream5876
person.familyNameSilva
person.givenNameDina
person.identifier.orcid0000-0003-4437-2765
person.identifier.scopus-author-id26657734400
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.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
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
rcaap.typearticle
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