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dc.rights.licenseAttribution-NonCommercial 4.0 International*
dc.contributor.authorCernuda García, Carlos
dc.contributor.authorEzpeleta Gallastegi, Enaitz
dc.contributor.authorAlberdi Aramendi, Ane
dc.contributor.otherGorostiza, Ania
dc.contributor.otherIbarrondo Olagüenaga, Oliver
dc.contributor.otherMar Medina, Javier
dc.contributor.otherArrospide, Arantzazu
dc.contributor.otherIruin, Álvaro
dc.contributor.otherLarrañaga Uribeetxeberria, Igor
dc.contributor.otherTainta, Mikel
dc.date.accessioned2020-09-02T07:16:56Z
dc.date.available2020-09-02T07:16:56Z
dc.date.issued2020
dc.identifier.issn1387-2877 Printen
dc.identifier.issn1875-8908 Onlineen
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=159492en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/1808
dc.description.abstractBackground: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated. Objective: The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decisionmakers. Methods: First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models. Results: Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age. Conclusion: Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.en
dc.description.sponsorshipDiputación Foral de Gipuzkoaes
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherIOS Pressen
dc.rights© 2020, IOS Press and the authorsen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectdementiaen
dc.subjectdepressive symptomsen
dc.subjectmachine learningen
dc.subjectneuropsychiatric symptomsen
dc.subjectpredictive modelen
dc.subjectprevalenceen
dc.subjectpsychotic symptomsen
dc.subjectreal-world dataen
dc.titleValidation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Dataen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.sourceJournal of Alzheimer's Diseaseen
dc.description.versioninfo:eu-repo/semantics/publishedVersionen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3233/JAD-200345en
local.relation.projectIDDFG/Programa Adinberri 2018//GIP/Construcción de un modelo predictivo de riesgo de trastornos de conducta y psicológicos asociados a la demencia mediante Machine Learning/PROTECTen
local.relation.projectIDGV/Convocatoria de ayudas a proyectos de investigación en enfermedades neurodegenerativas de BIOEF 2017/BIO17-ND-015/CAPV/Prevalencia y carga económica de los transtornos de conducta y psicológicos (TCPS) en la demencia obtenidas mediante análisis epidemiológico, estadístico y de machine learning/en
local.rights.publicationfeeAPCen
local.rights.publicationfeeamount1250 EURen
local.contributor.otherinstitutionOrganización Sanitaria Integrada Alto Debaes
local.contributor.otherinstitutionInstituto de Investigación en Servicios de Salud Kronikgunees
local.contributor.otherinstitutionInstituto de Investigación Sanitaria Biodonostiaes
local.contributor.otherinstitutionRed de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC)es
local.contributor.otherinstitutionRed de Salud Mental de Gipuzkoaes
local.contributor.otherinstitutionOrganización Sanitaria Integrada Goierri-Urola Garaiaes
local.contributor.otherinstitutionCentro de Investigación y Terapias Avanzadas para la enfermedad de Alzheimer (Fundación CITA Alzheimer)es
local.source.detailsVol. Pre-press. N. Pre-press, pp. 1-10, 2021en


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