ArticleScientific reports2020
Data-driven analysis using multiple self-report questionnaires to identify college students at high risk of depressive disorder.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- Investigating psychotherapists' attitudes towards artificial intelligence in psychotherapy.BMC psychology · 2025Article
- Application of functional near-infrared spectroscopy and machine learning to predict treatment response after six months in major depressive disorder.Translational psychiatry · 2025Article
- Screening for frequent hospitalization risk among community-dwelling older adult between 2016 and 2023: machine learning-driven item selection, scoring system development, and prospective validation.Frontiers in public health · 2024Article
- Impact of Perceived Body Weight on Depression, Anxiety and Stress Levels of Young Adults in Turkey.Iranian journal of public health · 2023Article
- The diagnostic and predictive potential of personality traits and coping styles in major depressive disorder.BMC psychiatry · 2022Article
- Prevalence of anxiety, depression, and stress among students of Jahangirnagar University in Bangladesh.Health science reports · 2022Article
- Characterization of Maternal Psychosocial Stress During Pregnancy: The Healthy Start Study.Women's health reports (New Rochelle, N.Y.) · 2022Article
- A Bayesian nonparametric approach to dynamic item-response modeling: An application to the GUSTO cohort study.Statistics in medicine · 2021Article
- Deep graph neural network-based prediction of acute suicidal ideation in young adults.Scientific reports · 2021Article
- Prevalence and predictors of anxiety, depression and stress among university students during the period of the first lockdown in Germany.Journal of affective disorders reports · 2021Article
- Taiwanese Depression Questionnaire and AD8 Questionnaire for Screening Late-Life Depression in Communities.Neuropsychiatric disease and treatment · 2021Article
- Validity of self-reporting depression in the Tabari cohort study population.Neuropsychopharmacology reports · 2020Article
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4 authors.
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Abstract
Depression diagnosis is one of the most important issues in psychiatry. Depression is a complicated mental illness that varies in symptoms and requires patient cooperation. In the present study, we demonstrated a novel data-driven attempt to diagnose depressive disorder based on clinical questionnaires. It includes deep learning, multi-modal representation, and interpretability to overcome the limitations of the data-driven approach in clinical application. We implemented a shared representation model between three different questionnaire forms to represent questionnaire responses in the same latent space. Based on this, we proposed two data-driven diagnostic methods; unsupervised and semi-supervised. We compared them with a cut-off screening method, which is a traditional diagnostic method for depression. The unsupervised method considered more items, relative to the screening method, but showed lower performance because it maximized the difference between groups. In contrast, the semi-supervised method adjusted for bias using information from the screening method and showed higher performance. In addition, we provided the interpretation of diagnosis and statistical analysis of information using local interpretable model-agnostic explanations and ordinal logistic regression. The proposed data-driven framework demonstrated the feasibility of analyzing depressed patients with items directly or indirectly related to depression.
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