ArticleInternational journal of environmental research and public health2021
Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Suicidal incidence and gender-based discrepancies in prolonged grief disorder: insights from a meta-analysis and systematic review.Frontiers in psychiatry · 2024Pooled it
- Suicide risk and mortality among patients with cancer.Nature medicine · 2022Pooled it
- Predicting suicidal ideation in academic communities using machine learning methods: a cross-sectional study.Lancet regional health. Americas · 2026Article
- Pathways to Future Depression Among University Students: The Role of Interpersonal Needs, Emotion Regulation and Meaning in Life.International journal of methods in psychiatric research · 2026Article
- Social support and life-threatening behavior in online psychotherapy patients: a possible "pulling-together effect" during COVID-19.Psicologia, reflexao e critica : revista semestral do Departamento de Psicologia da UFRGS · 2026Article
- Intraindividual Variability in Perceptual-Motor Performance Measured with Virtual Reality Among Military Veterans.Brain sciences · 2026Article
- Suicidal Ideation in Spinocerebellar Ataxia.The Journal of neuropsychiatry and clinical neurosciences · 2026Article
- Analysis of Multi-Domain Factors Influencing Mental Health and Suicide: A Comparative Study of Urban and Rural Areas at City, County, and District Levels in Korea.Journal of Korean medical science · 2025Article
- Suicide mortality and suicidal ideation among patients with colorectal cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Examination of the Potential Moderating Role of Psychological Wellbeing in the Relationship Between Depression and Thoughts of Self-Harm in Autistic Adolescents and Adults: A Two-Year Longitudinal Study.Journal of autism and developmental disorders · 2025Article
- Evaluating Factors Associated with Suicidal Ideation Among Healthcare Workers: A Secondary Analysis.Journal of general internal medicine · 2025Article
- Article
- Review
- Suicidal ideation among mental health patients at hospital discharge: prevalence and risk factors.BMC psychiatry · 2025Article
- Sociodemographic and clinical determinants of suicidal ideation: an analysis in a Peruvian consultation-liaison psychiatry unit.BMC psychiatry · 2025Article
- The prevalence and risk factors of sleep disturbances among mental health patients following hospital discharge.Frontiers in public health · 2025Article
- Prevalence and determinants of suicidal ideation among South African Health Sciences students at the onset of the COVID-19 pandemic.Frontiers in psychiatry · 2025Article
- Recognising and Responding to Suicide-Risk Factors in Primary Care: A Scoping Review.Journal of prevention (2022) · 2024Article
- Risk for Suicide Attempts Assessed Using the Patient Health Questionnaire-9 Modified for Teens.JAMA network open · 2024Article
- Anxiety and depression symptoms in adolescents and young adults with juvenile idiopathic arthritis: results of an outpatient screening.Arthritis research & therapy · 2024Observational
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: The Patient Health Questionnaire-9 (PHQ-9) is a tool that screens patients for depression in primary care settings. In this study, we evaluated the efficacy of PHQ-9 in evaluating suicidal ideation (2) Methods: A total of 8760 completed questionnaires collected from college students were analyzed. The PHQ-9 was scored in combination with and evaluated against four categories (PHQ-2, PHQ-8, PHQ-9, and PHQ-10). Suicidal ideations were evaluated using the Mini-International Neuropsychiatric Interview suicidality module. Analyses used suicide ideation as the dependent variable, and machine learning (ML) algorithms, k-nearest neighbors, linear discriminant analysis (LDA), and random forest. (3) Results: Random forest application using the nine items of the PHQ-9 revealed an excellent area under the curve with a value of 0.841, with 94.3% accuracy. The positive and negative predictive values were 84.95% (95% CI = 76.03-91.52) and 95.54% (95% CI = 94.42-96.48), respectively. (4) Conclusion: This study confirmed that ML algorithms using PHQ-9 in the primary care field are reliably accurate in screening individuals with suicidal ideation.
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