ArticleEpidemiology and psychiatric sciences2023
Frequency and machine learning predictors of severe depressive symptoms and suicidal ideation among university students.
Article in Epidemiology and psychiatric sciences, 2023. 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, 23 citations in OpenAlex.
- Instruments used to measure well-being, ill-being, and health-related lifestyle behaviors in students attending Italian universities: a systematic review.Frontiers in public health · 2026Pooled it
- How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review.Cadernos de saude publica · 2024Pooled it
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- Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights.MethodsX · 2025Review
- Predicting Suicidal Ideation in Community-based Older Adults Using Self-report Questionnaires with Machine Learning.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025Article
- Predicting generalized anxiety disorder among Chinese depressed adolescents: an explainable machine learning approach.BMC medical informatics and decision making · 2025Article
- Prevalence and risk factors of depressive and anxiety symptoms and functional constipation among university students in Eastern China.World journal of psychiatry · 2025Article
- Identifying suicidal ideation in Chinese higher vocational students using machine learning: a cross-sectional survey.European archives of psychiatry and clinical neuroscience · 2025Article
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Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsProspective studies on the mental health of university students highlighted a major concern. Specifically, young adults in academia are affected by markedly worse mental health status than their peers or adults in other vocations. This situation predisposes to exacerbated disability-adjusted life-years.
methodsWe enroled 1,388 students at the baseline, 557 of whom completed follow-up after 6 months, incorporating their demographic information and self-report questionnaires on depressive, anxiety and obsessive-compulsive symptoms. We applied multiple regression modelling to determine associations - at baseline - between demographic factors and self-reported mental health measures and supervised machine learning algorithms to predict the risk of poorer mental health at follow-up, by leveraging the demographic and clinical information collected at baseline.
resultsApproximately one out of five students reported severe depressive symptoms and/or suicidal ideation. An association of economic worry with depression was evidenced both at baseline (when high-frequency worry odds ratio = 3.11 [1.88-5.15]) and during follow-up. The random forest algorithm exhibited high accuracy in predicting the students who maintained well-being (balanced accuracy = 0.85) or absence of suicidal ideation but low accuracy for those whose symptoms worsened (balanced accuracy = 0.49). The most important features used for prediction were the cognitive and somatic symptoms of depression. However, while the negative predictive value of worsened symptoms after 6 months of enrolment was 0.89, the positive predictive value is basically null.
conclusionsStudents' severe mental health problems reached worrying levels, and demographic factors were poor predictors of mental health outcomes. Further research including people with lived experience will be crucial to better assess students' mental health needs and improve the predictive outcome for those most at risk of worsening symptoms.
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