ArticlePsychiatry investigation2021
Classification of Adolescent Psychiatric Patients at High Risk of Suicide Using the Personality Assessment Inventory by Machine Learning.
Article in Psychiatry investigation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Application and effectiveness of artificial intelligence in adolescent mental health support and therapy: a scoping review.BMC psychiatry · 2026Article
- Predicting suicide attempts in a high-risk clinical cohort of adolescents using machine-learning.European child & adolescent psychiatry · 2026Article
- Multimodal Machine Learning Prediction of 12-Month Suicide Attempts in Bipolar Disorder.Bipolar disorders · 2025Article
- Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic.Scientific reports · 2025Article
- Diligent for better or worse: Conscientiousness is associated with higher likelihood of suicidal behavior and more severe suicidal intent in later life.Comprehensive psychiatry · 2024Article
- Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States.Healthcare (Basel, Switzerland) · 2024Article
- Risk Factors and Trends in Adolescent's Suicide Attempt Rates Before and After the Coronavirus Disease 2019 Pandemic.Journal of Korean medical science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThere are growing interests on suicide risk screening in clinical settings and classifying high-risk groups of suicide with suicidal ideation is crucial for a more effective suicide preventive intervention. Previous statistical techniques were limited because they tried to predict suicide risk using a simple algorithm. Machine learning differs from the traditional statistical techniques in that it generates the most optimal algorithm from various predictors.
methodsWe aim to analyze the Personality Assessment Inventory (PAI) profiles of child and adolescent patients who received outpatient psychiatric care using machine learning techniques, such as logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and extreme gradient boosting (XGB), to develop and validate a classification model for individuals with high suicide risk.
resultsWe developed prediction models using seven relevant features calculated by Boruta algorithm and subsequently tested all models using the testing dataset. The area under the ROC curve of these models were above 0.9 and the RF model exhibited the best performance.
conclusionSuicide must be assessed based on multiple aspects, and although Personality Assessment Inventory for Adolescent assess an array of domains, further research is needed for predicting high suicide risk groups.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.