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 fortythree 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.

Objective:To identify clinical and sociodemographic factors that increase or decrease suicidal risk in a clinical sample of subjects seeking mental health care.Method:A cross-sectional study was performed at three health centers in Santiago, Chile. The Parental Bonding Instrument (PBI), Depressive Experience Questionnaire (DEQ), Outcome Questionnaire (OQ-45.2), Reasons for Living Inventory (RFL), and State Trait Anger Expression Inventory (STAXI-2), in addition to a sociodemographic survey, were applied to 544 participants (333 with suicidal behavior and 211 without current suicidal behavior). Through hierarchical clustering analysis, participants were grouped by similarity regarding suicidal risk. Then, a regression analysis was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) technique, and factors that decrease or increase suicide risk (SR) were identified for each cluster.Results:The resultant clusters were grouped mainly by the age of participants. The most important protective factor was having confidence in one’s own coping skills in difficult situations. Relevant risk factors were major depressive disorder (MDD), poor anger management, and difficulties in interpersonal relationships.Conclusions:Suicidal risk manifests differently throughout the life cycle, and different types of bonds may protect from or increase risk of suicide.

This study aimed to determine conditional dependence relationships for variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian Network was developed and applied to establish conditional dependence relationships among variables for each individual subject studied. These conditional dependencies represented the different states that patients could have adopted in relation to suicidal behavior (SB). The clinical sample included 650 mental health patients with mood and anxiety symptomatology. Results mainly indicated that variables within the Bayesian Network are part of each patient’s state of psychological vulnerability, having the potential to impact such states, and that variables coexist and are relatively stable over time. These results have enabled us to offer a tool to detect states of psychological vulnerability associated with suicide risk. If we accept that suicidal behaviors (vulnerability-ideation-suicidal attempts) exist in constant change and are unstable, we can investigate what happens to individuals at specific moments as doorways toward intervening in a timely manner so as to prevent such behaviors. Future testing of the tool developed in this study is needed, not only in specialized mental health environments, but also in other places with high rates of mental illness such as primary healthcare facilities and educational institutions (schools and universities), among others.

Background: This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional dependence relationships among variables for each individual subject studied. These conditional dependencies represented the different states that patients could experience in relation to suicidal behavior (SB). The clinical sample included 650 mental health patients with mood and anxiety symptomatology. Results: Mainly indicated that variables within the Bayesian network are part of each patient's state of psychological vulnerability and have the potential to impact such states and that these variables coexist and are relatively stable over time. These results have enabled us to offer a tool to detect states of psychological vulnerability associated with suicide risk. Conclusion: If we accept that suicidal behaviors (vulnerability, ideation, and suicidal attempts) exist in constant change and are unstable, we can investigate what individuals experience at specific moments to become better able to intervene in a timely manner to prevent such behaviors. Future testing of the tool developed in this study is needed, not only in specialized mental health environments but also in other environments with high rates of mental illness, such as primary healthcare facilities and educational institutions.

This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional dependence relationships among variables for each individual subject studied. These conditional dependencies represented the different states that patients could experience in relation to suicidal behavior (SB). The clinical sample included 650 mental health patients with mood and anxiety symptomatology. The results mainly indicated that variables within the Bayesian network are part of each patient’s state of psychological vulnerability and have the potential to impact such states and that these variables coexist and are relatively stable over time. These results have enabled us to offer a tool to detect states of psychological vulnerability associated with suicide risk. If we accept that suicidal behaviors (vulnerability, ideation, and suicidal attempts) exist in constant change and are unstable, we can investigate what individuals experience at specific moments to become better able to intervene in a timely manner to prevent such behaviors. Future testing of the tool developed in this study is needed, not only in specialized mental health environments but also in other environments with high rates of mental illness, such as primary healthcare facilities and educational institutions (schools and universities).