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Machine learning detects predictors of symptom severity and impulsivity after dialectical behavior therapy skills training group in borderline personality disorder

dc.creatorJiménez, Saides_ES
dc.creatorAngeles-Valdez, Diegoes_ES
dc.creatorRodríguez-Delgado, Andréses_ES
dc.creatorFresán, Anaes_ES
dc.creatorMiranda, Edgares_ES
dc.creatorAlcalá-Lozano, Ruthes_ES
dc.creatorDuque-Alarcón, Xóchitles_ES
dc.creatorArango de Montis, Ivánes_ES
dc.creatorGarza-Villarreal, Eduardo A.es_ES
dc.date2022
dc.date.accessioned2024-12-11T17:53:07Z
dc.date.available2024-12-11T17:53:07Z
dc.date.issued2022
dc.identifierOE10IC22es_ES
dc.identifier.issn0022-3956
dc.identifier.urihttp://repositorio.inprf.gob.mx/handle/123456789/8152
dc.identifier.urihttps://doi.org/10.1016/j.jpsychires.2022.03.063
dc.descriptionOnly 50% of the patients with Borderline Personality Disorder (BPD) respond to psychotherapies, such as Dialectical Behavioral Therapy (DBT), this might be increased by identifying baseline predictors of clinical change. We use machine learning to detect clinical features that could predict improvement/worsening for severity and impulsivity of BPD after DBT skills training group. To predict illness severity, we analyzed data from 125 patients with BPD divided into 17 DBT psychotherapy groups, and for impulsiveness we analyzed 89 patients distributed into 12 DBT groups. All patients were evaluated at baseline using widely self-report tests; ∼70% of the sample were randomly selected and two machine learning models (lasso and Random forest [Rf]) were trained using 10-fold cross-validation and compared to predict the post-treatment response. Models' generalization was assessed in ∼30% of the remaining sample. Relevant variables for DBT (i.e. the mindfulness ability "non-judging", or "non-planning" impulsiveness) measured at baseline, were robust predictors of clinical change after six months of weekly DBT sessions. Using 10-fold cross-validation, the Rf model had significantly lower prediction error than lasso for the BPD severity variable, Mean Absolute Error (MAE) lasso - Rf = 1.55 (95% CI, 0.63-2.48) as well as for impulsivity, MAE lasso - Rf = 1.97 (95% CI, 0.57-3.35). According to Rf and the permutations method, 34/613 significant predictors for severity and 17/613 for impulsivity were identified. Using machine learning to identify the most important variables before starting DBT could be fundamental for personalized treatment and disease prognosis.es_ES
dc.formatPDFes_ES
dc.language.isoenges_ES
dc.publisherPergamon Presses_ES
dc.relation151:42-49
dc.rightsAcceso Cerradoes_ES
dc.titleMachine learning detects predictors of symptom severity and impulsivity after dialectical behavior therapy skills training group in borderline personality disorderes_ES
dc.typeArtículoes_ES
dc.contributor.affiliationFacultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, Mexico
dc.contributor.emailsaid.ejp@comunidad.unam.mx (S. Jiménez) ; egarza@comunidad.unam.mx (E.A. Garza-Villarreal)
dc.relation.jnabreviadoJ PSYCHIATR RES
dc.relation.journalJournal of Psychiatric Research
dc.identifier.placeInglaterra
dc.date.published2022
dc.identifier.organizacionInstituto Nacional de Psiquiatría Ramón de la Fuente Muñiz
dc.identifier.eissn1879-1379
dc.identifier.doi10.1016/j.jpsychires.2022.03.063
dc.subject.kwBorderline personality disorder
dc.subject.kwDialectical behavioral therapy
dc.subject.kwImpulsivity
dc.subject.kwLasso
dc.subject.kwMachine learning
dc.subject.kwRandom forest


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