ArticleInternational journal of methods in psychiatric research2021
Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches.
Article in International journal of methods in psychiatric research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Predictive Validity and Cutoff Scores of the Revised Suicide Crisis Inventory in Korean Adults: A One-Year Follow-Up Study.Psychiatry investigation · 2025Article
- Suicide Crisis Syndrome: examining supporting evidence and barriers to diagnostic validity.Frontiers in psychiatry · 2025Article
- Examining suicide risk among people with schizophrenia, focusing on the role of anosognosia and the ethical considerations for future research directions.Frontiers in psychiatry · 2025Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.Scientific reports · 2024Article
- Article
- Prospective Validation of a Suicide Event Risk Model in Transgender Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveThis study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI), which measures the Suicide Crisis Syndrome, a presuicidal mental state.
methodsSCI data were collected from high-risk psychiatric inpatients (N = 591) grouped based on their short-term suicidal behavior, that is, those who attempted suicide between intake and 1-month follow-up dates (N = 20) and those who did not (N = 571). Data were analyzed using three predictive algorithms (logistic regression, random forest, and gradient boosting) and three sampling approaches (split sample, Synthetic minority oversampling technique, and enhanced bootstrap).
resultsThe enhanced bootstrap approach considerably outperformed the other sampling approaches, with random forest (98.0% precision; 33.9% recall; 71.0% Area under the precision-recall curve [AUPRC]; and 87.8% Area under the receiver operating characteristic [AUROC]) and gradient boosting (94.0% precision; 48.9% recall; 70.5% AUPRC; and 89.4% AUROC) algorithms performing best in predicting positive cases of near-term suicidal behavior using this dataset.
conclusionsML can be useful in analyzing data from psychometric scales, such as the SCI, and for predicting near-term suicidal behavior. However, in cases such as the current analysis where the data are highly imbalanced, the optimal method of measuring performance must be carefully considered and selected.
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