Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , , , |
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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Brazilian Journal of Psychiatry (São Paulo. 1999. Online) |
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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 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462017000100001 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462017000100001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1516-4446-2015-1877 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
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) instacron:ABP |
instname_str |
Associação Brasileira de Psiquiatria (ABP) |
instacron_str |
ABP |
institution |
ABP |
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 |
_version_ |
1754212557357318144 |