Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning.
Background: The use of machine learning (ML) algorithms to study suicidality has recently been recommended. Our aim was to explore whether ML approaches have the potential to improve the prediction of suicide attempt (SA) risk. Using the epidemiological multiwave prospective-longitudinal Early Devel...
| Publicado en: | Journal of Affective Disorders Vol. 265; pp. 570 - 579 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141828342&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141828342 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01650327 3M9 jtl: Journal of Affective Disorders issn: 01650327 maglogo: N pubinfo: dt: Mar2020 vid: 265 pid: 1004 pub: Elsevier B.V. artinfo: ui: 141828342 141828342 NLM31786028 141828342 10.1016/j.jad.2019.11.093 NLM31786028 141828342 ppf: 570 ppct: 9 formats: tig: atl: Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning. aug: au: Miché, Marcel Studerus, Erich Meyer, Andrea Hans Gloster, Andrew Thomas Beesdo-Baum, Katja Wittchen, Hans-Ulrich Lieb, Roselind affil: University of Basel, Department of Psychology, Division of Clinical Psychology and Epidemiology, Basel, Switzerland sug: subj: Suicide, Attempted Logistic Regression Young Adult Prospective Studies Regression Human Adolescence Adult Validation Studies Comparative Studies Evaluation Research Multicenter Studies Adolescent: 13-18 years Adult: 19-44 years ab: Background: The use of machine learning (ML) algorithms to study suicidality has recently been recommended. Our aim was to explore whether ML approaches have the potential to improve the prediction of suicide attempt (SA) risk. Using the epidemiological multiwave prospective-longitudinal Early Developmental Stages of Psychopathology (EDSP) data set, we compared four algorithms-logistic regression, lasso, ridge, and random forest-in predicting a future SA in a community sample of adolescents and young adults.Methods: The EDSP Study prospectively assessed, over the course of 10 years, adolescents and young adults aged 14-24 years at baseline. Of 3021 subjects, 2797 were eligible for prospective analyses because they participated in at least one of the three follow-up assessments. Sixteen baseline predictors, all selected a priori from the literature, were used to predict follow-up SAs. Model performance was assessed using repeated nested 10-fold cross-validation. As the main measure of predictive performance we used the area under the curve (AUC).Results: The mean AUCs of the four predictive models, logistic regression, lasso, ridge, and random forest, were 0.828, 0.826, 0.829, and 0.824, respectively.Conclusions: Based on our comparison, each algorithm performed equally well in distinguishing between a future SA case and a non-SA case in community adolescents and young adults. When choosing an algorithm, different considerations, however, such as ease of implementation, might in some instances lead to one algorithm being prioritized over another. Further research and replication studies are required in this regard. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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