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dc.creatorVan Loo, H.M.
dc.creatorCai, T.X.
dc.creatorGruber, M.J.
dc.creatorLi, J.L.
dc.creatorDe Jonge, P.
dc.creatorPetukhova, M.
dc.creatorRose, S.
dc.creatorSampson, N.A.
dc.creatorSchoevers, R.A.
dc.creatorWardenaar, K.J.
dc.creatorWilcox, M.A.
dc.creatorAl-Hamzawi, A.O.
dc.creatorAndrade, L.H.
dc.creatorBromet, E.J.
dc.creatorBunting, B.
dc.creatorFayyad, J.
dc.creatorFlorescu, S.E.
dc.creatorGureje, O.
dc.creatorHu, C.Y.
dc.creatorHuang, Y.Q.
dc.creatorLevinson, D.
dc.creatorMedina-Mora, M.E.
dc.creatorNakane, Y.
dc.creatorPosada-Villa, J.
dc.creatorScott, K. M.
dc.creatorXavier, M.
dc.creatorZarkov, Z.
dc.creatorKessler, R.C.
dc.date.accessioned2017-06-29T03:57:28Z
dc.date.available2017-06-29T03:57:28Z
dc.date.issued2014es_ES
dc.identifier2808es_ES
dc.identifier.issn1091-4269es_ES
dc.identifier.urihttps://doi.org/10.1002/da.22233es_ES
dc.identifier.urihttp://repositorio.inprf.gob.mx/handle/123456789/4657
dc.description.abstractes_ES
dc.language.isoenges_ES
dc.publisherNew York, NY : Wileyes_ES
dc.relation31 (9) 765-777 p.es_ES
dc.relationversión del editores_ES
dc.rightsacceso cerradoes_ES
dc.subject.meshAdolescentes_ES
dc.subject.meshAdultes_ES
dc.subject.meshAgedes_ES
dc.subject.meshAmericas/epidemiologyes_ES
dc.subject.meshAsia/epidemiologyes_ES
dc.subject.meshCluster Analysises_ES
dc.subject.meshData Mining/methodses_ES
dc.subject.meshDepressive Disorder, Major/classificationes_ES
dc.subject.meshDepressive Disorder, Major/epidemiologyes_ES
dc.subject.meshEurope/epidemiologyes_ES
dc.subject.meshHumanses_ES
dc.subject.meshMiddle Agedes_ES
dc.subject.meshNew Zealand/epidemiologyes_ES
dc.subject.meshNigeria/epidemiologyes_ES
dc.subject.meshPrognosises_ES
dc.subject.meshSeverity of Illness Indexes_ES
dc.subject.meshYoung Adultes_ES
dc.titleMajor depresive disorder subtypes to predict long-term coursees_ES
dc.title.alternativees_ES
dc.typeartículoes_ES
dc.contributor.affiliationHarvard Univ, Sch Med, Dept Hlth Care Policy, 180 Longwood Ave, Boston, MA 02115 USA.es_ES
dc.contributor.emailNCS@hcp.med.harvard.edues_ES
dc.relation.jnabreviadoDEPRESS ANXIETYes_ES
dc.relation.journalDepression and Anxietyes_ES
dc.identifier.placeEstados Unidoses_ES
dc.date.published2014es_ES
dc.identifier.organizacionInstituto Nacional de Psiquiatría Ramón de la Fuente Muñizes_ES
dc.identifier.eissn1520-6394es_ES
dc.identifier.doi10.1002/da.22233es_ES
dc.description.monthSepes_ES
dc.description.abstractotrodiomaBackground: Variation in the course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here.   Methods: Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs), followed by k-means cluster analysis are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode, number of years with an episode lasting most of the year) and severity (hospitalization for MDD, disability due to MDD). Results: Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity, and it was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor.   Conclusions: Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data. (C) 2014 Wiley Periodicals, Inc.  es_ES
dc.subject.meshmes_ES
dc.subject.kwes_ES
dc.subject.koDespaires_ES
dc.subject.koForced swimming testes_ES
dc.subject.koRatses_ES
dc.subject.koSerotonin transporteres_ES
dc.subject.koAge differenceses_ES
dc.subject.koEpidemiologyes_ES
dc.subject.koDepressiones_ES
dc.subject.koAnxiety/Anxiety Disorderses_ES
dc.subject.koSuicide-Self Harmes_ES
dc.subject.koPanic Attackses_ES


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