Artículos de revistas
Suicide detection in Chile: Proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders
Fecha
2017Registro en:
Revista Brasileira de Psiquiatria, Volumen 39, Issue 1, 2018, Pages 1-11
15164446
10.1590/1516-4446-2015-1877
Autor
Barros, Jorge
Morales, Susana
Echávarri, Orietta
García, Arnol
Ortega, Jaime
Asahi, Takeshi
Moya, Claudia
Fischman, Ronit
Maino, María P.
Núñez, Catalina
Institución
Resumen
© 2017, Associacao Brasileira de Psiquiatria. All rights reserved. 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 con