ArticleRevista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)
Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders.
Article in Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 5 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 5 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.BMC medical informatics and decision making · 2024Pooled it
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.Frontiers in digital health · 2022Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.Journal of medical systems · 2018Pooled it
- Review
- Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec.PloS one · 2024Article
- Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons.PeerJ. Computer science · 2024Article
- Development of a Checklist for Predicting Suicidality Based on Risk and Protective Factors: The Gwangju Checklist for Evaluation of Suicidality.Psychiatry investigation · 2022Article
- Influence of Short Video Application on College Students' Mental Health under Big Data Monitoring Environment.Journal of environmental and public health · 2022Article
- Empirical analysis based on the related factors of college students' mental health problems.Frontiers in psychology · 2022Article
- Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.BMC psychiatry · 2020Article
- Suicidal Ideation Mediates the Relationship Between Affect and Suicide Attempt in Adolescents.Frontiers in psychology · 2020Article
- Pre-disaster PTSD as a moderator of the relationship between natural disaster and suicidal ideation over time.Journal of affective disorders · 2018Article
- Article
- Acute Mental Discomfort Associated with Suicide Behavior in a Clinical Sample of Patients with Affective Disorders: Ascertaining Critical Variables Using Artificial Intelligence Tools.Frontiers in psychiatry · 2017Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
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