ArticlePsychiatry investigation2022
Machine Learning on Early Diagnosis of Depression.
Article in Psychiatry investigation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
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- Network analysis of anxiety, depression, stress, and sleep disturbances in healthcare workers.Frontiers in psychiatry · 2026Article
- AI Applications in Depression Detection and Diagnosis: Bibliometric and Visual Analysis of Trends and Future Directions.JMIR mental health · 2025Article
- Optimal mean arterial pressure for favorable neurological outcomes in patients after cardiac arrest.Journal of intensive care · 2025Article
- Association between blood transfusion and early mortality in patient undergoing extracorporeal membrane oxygenation.Scientific reports · 2025Article
- Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.JMIR mental health · 2025Article
- Graph Machine Learning With Systematic Hyper-Parameter Selection on Hidden Networks and Mental Health Conditions in the Middle-Aged and Old.Psychiatry investigation · 2024Article
- Clinical and dental predictors of preterm birth using machine learning methods: the MOHEPI study.Scientific reports · 2024Article
- Article
- Exploration of a Machine Learning Model Using Self-rating Questionnaires for Detecting Depression in Patients with Breast Cancer.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2024Article
- Artificial intelligence in colonoscopy: from detection to diagnosis.The Korean journal of internal medicine · 2024Review
- Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.Heliyon · 2024Review
- Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.Scientific reports · 2024Article
- Explainable Model Using Shapley Additive Explanations Approach on Wound Infection after Wide Soft Tissue Sarcoma Resection: "Big Data" Analysis Based on Health Insurance Review and Assessment Service Hub.Medicina (Kaunas, Lithuania) · 2024Article
- Biopsychosocial phenotypes in people with HIV in the CHARTER cohort.Brain communications · 2024Article
- Article
- Relationships of Antidepressant Medication With Its Various Factors Including Nitrogen Dioxides Seasonality: Machine Learning Analysis Using National Health Insurance Data.Psychiatry investigation · 2023Article
- Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective.Journal of Alzheimer's disease : JAD · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were "depression" (title) and "random forest" (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1-100.0 for accuracy and 64.0-96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression.
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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.