Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders

Detalhes bibliográficos
Autor(a) principal: Barros,Jorge
Data de Publicação: 2017
Outros Autores: Morales,Susana, Echávarri,Orietta, García,Arnol, Ortega,Jaime, Asahi,Takeshi, Moya,Claudia, Fischman,Ronit, Maino,María P., Núñez,Catalina
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Psychiatry (São Paulo. 1999. Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462017000100001
Resumo: Objective: To analyze suicidal behavior and build a predictive model for suicide risk using data mining (DM) analysis. Methods: A study of 707 Chilean mental health patients (with and without suicide risk) was carried out across three healthcare centers in the Metropolitan Region of Santiago, Chile. Three hundred forty-three variables were studied using five questionnaires. DM and machine-learning tools were used via the support vector machine technique. Results: The model selected 22 variables that, depending on the circumstances in which they all occur, define whether a person belongs in a suicide risk zone (accuracy = 0.78, sensitivity = 0.77, and specificity = 0.79). Being in a suicide risk zone means patients are more vulnerable to suicide attempts or are thinking about suicide. The interrelationship between these variables is highly nonlinear, and it is interesting to note the particular ways in which they are configured for each case. The model shows that the variables of a suicide risk zone are related to individual unrest, personal satisfaction, and reasons for living, particularly those related to beliefs in one’s own capacities and coping abilities. Conclusion: These variables can be used to create an assessment tool and enables us to identify individual risk and protective factors. This may also contribute to therapeutic intervention by strengthening feelings of personal well-being and reasons for staying alive. Our results prompted the design of a new clinical tool, which is fast and easy to use and aids in evaluating the trajectory of suicide risk at a given moment.
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spelling Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disordersSuicidemood disordersdata mining Objective: To analyze suicidal behavior and build a predictive model for suicide risk using data mining (DM) analysis. Methods: A study of 707 Chilean mental health patients (with and without suicide risk) was carried out across three healthcare centers in the Metropolitan Region of Santiago, Chile. Three hundred forty-three variables were studied using five questionnaires. DM and machine-learning tools were used via the support vector machine technique. Results: The model selected 22 variables that, depending on the circumstances in which they all occur, define whether a person belongs in a suicide risk zone (accuracy = 0.78, sensitivity = 0.77, and specificity = 0.79). Being in a suicide risk zone means patients are more vulnerable to suicide attempts or are thinking about suicide. The interrelationship between these variables is highly nonlinear, and it is interesting to note the particular ways in which they are configured for each case. The model shows that the variables of a suicide risk zone are related to individual unrest, personal satisfaction, and reasons for living, particularly those related to beliefs in one’s own capacities and coping abilities. Conclusion: These variables can be used to create an assessment tool and enables us to identify individual risk and protective factors. This may also contribute to therapeutic intervention by strengthening feelings of personal well-being and reasons for staying alive. Our results prompted the design of a new clinical tool, which is fast and easy to use and aids in evaluating the trajectory of suicide risk at a given moment.Associação Brasileira de Psiquiatria2017-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462017000100001Brazilian Journal of Psychiatry v.39 n.1 2017reponame:Brazilian Journal of Psychiatry (São Paulo. 1999. Online)instname:Associação Brasileira de Psiquiatria (ABP)instacron:ABP10.1590/1516-4446-2015-1877info:eu-repo/semantics/openAccessBarros,JorgeMorales,SusanaEchávarri,OriettaGarcía,ArnolOrtega,JaimeAsahi,TakeshiMoya,ClaudiaFischman,RonitMaino,María P.Núñez,Catalinaeng2017-06-01T00:00:00Zoai:scielo:S1516-44462017000100001Revistahttp://www.bjp.org.br/ahead_of_print.asphttps://old.scielo.br/oai/scielo-oai.php||rbp@abpbrasil.org.br1809-452X1516-4446opendoar:2017-06-01T00:00Brazilian Journal of Psychiatry (São Paulo. 1999. Online) - Associação Brasileira de Psiquiatria (ABP)false
dc.title.none.fl_str_mv Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
title Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
spellingShingle Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
Barros,Jorge
Suicide
mood disorders
data mining
title_short Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
title_full Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
title_fullStr Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
title_full_unstemmed Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
title_sort Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
author Barros,Jorge
author_facet Barros,Jorge
Morales,Susana
Echávarri,Orietta
García,Arnol
Ortega,Jaime
Asahi,Takeshi
Moya,Claudia
Fischman,Ronit
Maino,María P.
Núñez,Catalina
author_role author
author2 Morales,Susana
Echávarri,Orietta
García,Arnol
Ortega,Jaime
Asahi,Takeshi
Moya,Claudia
Fischman,Ronit
Maino,María P.
Núñez,Catalina
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Barros,Jorge
Morales,Susana
Echávarri,Orietta
García,Arnol
Ortega,Jaime
Asahi,Takeshi
Moya,Claudia
Fischman,Ronit
Maino,María P.
Núñez,Catalina
dc.subject.por.fl_str_mv Suicide
mood disorders
data mining
topic Suicide
mood disorders
data mining
description Objective: To analyze suicidal behavior and build a predictive model for suicide risk using data mining (DM) analysis. Methods: A study of 707 Chilean mental health patients (with and without suicide risk) was carried out across three healthcare centers in the Metropolitan Region of Santiago, Chile. Three hundred forty-three variables were studied using five questionnaires. DM and machine-learning tools were used via the support vector machine technique. Results: The model selected 22 variables that, depending on the circumstances in which they all occur, define whether a person belongs in a suicide risk zone (accuracy = 0.78, sensitivity = 0.77, and specificity = 0.79). Being in a suicide risk zone means patients are more vulnerable to suicide attempts or are thinking about suicide. The interrelationship between these variables is highly nonlinear, and it is interesting to note the particular ways in which they are configured for each case. The model shows that the variables of a suicide risk zone are related to individual unrest, personal satisfaction, and reasons for living, particularly those related to beliefs in one’s own capacities and coping abilities. Conclusion: These variables can be used to create an assessment tool and enables us to identify individual risk and protective factors. This may also contribute to therapeutic intervention by strengthening feelings of personal well-being and reasons for staying alive. Our results prompted the design of a new clinical tool, which is fast and easy to use and aids in evaluating the trajectory of suicide risk at a given moment.
publishDate 2017
dc.date.none.fl_str_mv 2017-03-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1516-4446-2015-1877
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dc.publisher.none.fl_str_mv Associação Brasileira de Psiquiatria
publisher.none.fl_str_mv Associação Brasileira de Psiquiatria
dc.source.none.fl_str_mv Brazilian Journal of Psychiatry v.39 n.1 2017
reponame:Brazilian Journal of Psychiatry (São Paulo. 1999. Online)
instname:Associação Brasileira de Psiquiatria (ABP)
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instname_str Associação Brasileira de Psiquiatria (ABP)
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reponame_str Brazilian Journal of Psychiatry (São Paulo. 1999. Online)
collection Brazilian Journal of Psychiatry (São Paulo. 1999. Online)
repository.name.fl_str_mv Brazilian Journal of Psychiatry (São Paulo. 1999. Online) - Associação Brasileira de Psiquiatria (ABP)
repository.mail.fl_str_mv ||rbp@abpbrasil.org.br
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