ReviewHealthcare (Basel, Switzerland)2023
A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis.
Review in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
61 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.Frontiers in psychiatry · 2026Pooled it
- A systematic survey on the application of federated learning in mental state detection and human activity recognition.Frontiers in digital health · 2024Pooled it
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Machine learning and natural language processing for the identification of potential mental disorders among school-age children: a prospective birth cohort study.BMC medicine · 2026Article
- Calibrated Deep-Learning Risk Indexing and Latent Behavioural Profiling for Occupational Mental-Health Risk Assessment.Bioengineering (Basel, Switzerland) · 2026Article
- Early identification of major depression risk among reproductive-age women using hormonal contraceptives in southern Ethiopia: A Machine Learning Approach.Scientific reports · 2026Article
- Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.Scientific reports · 2026Article
- Innovations to improve outcomes and uptake of psychotherapies for mental disorders: a state-of-the-art review.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Cross platform social media analysis for mental health detection.Discover mental health · 2026Review
- Predicting substance use behaviors with machine learning using small sets of judgment and contextual variables.Npj mental health research · 2026Article
- Transformer architecture for diagnosing schizophrenia disabilities through EEG analysis.Frontiers in physiology · 2026Article
- Artificial Intelligence in Neuro-Ophthalmology for Optic Disc Pathologies and Neurodegenerative Disease.Eye and brain · 2026Review
- Predicting Ultra-High Risk Outcomes Using Linguistic and Acoustic Measures From High-Risk Social Challenge Recordings: mHealth Longitudinal Cohort Exploratory Study.JMIR formative research · 2025Article
- What machine learning teaches us about depression prediction across the life course: An exploratory comparison of predictive models.SSM - population health · 2025Article
- Quantifying Exploratory Behavior In The Human Behavioral Pattern Monitor Using Automated Video Tracking.Methods in Psychology (Online) · 2025Article
- StressSpeak: A Speech-Driven Framework for Real-Time Personalized Stress Detection and Adaptive Psychological Support.Diagnostics (Basel, Switzerland) · 2025Article
- Advancing mental health diagnostics: a review on the role of smartphones, wearable devices, and artificial intelligence in depression and anxiety detection.Biomedical engineering letters · 2025Review
- Article
- Machine learning-enabled detection of electrophysiological signatures in iPSC-derived models of schizophrenia and bipolar disorder.APL bioengineering · 2025Article
- Article
1 more citing papers are in PubMed but not listed here.
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.
Funding
Abstract
Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.