ReviewBehavioral sciences (Basel, Switzerland)2025
Systematic Review and Meta-Analysis of Explainable Machine Learning Models for Clinical Depression Detection.
Review in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis 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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for the occurrence and mortality of sepsis-associated lung injury: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Leakage-Free Multimodal Depression Screening: Controlled Evaluation of Text, Facial Behavior, and Prosodic Fusion.Bioengineering (Basel, Switzerland) · 2026Article
- Multimodal behavioral phenotyping for depressive-spectrum classification and severity estimation using eye tracking, facial behavior, and transcript-derived language.Frontiers in psychiatry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is among the most prevalent mental disorders, and its early detection is essential to improving therapeutic outcomes in psychotherapy. This systematic review and meta-analysis evaluated the accuracy, interpretability, and generalizability of supervised algorithms (SVM, Random Forest, XGBoost, and GCN) for clinical detection of depression using real-world data. Following PRISMA guidelines, 20 studies published between 2014 and 2025 were analyzed across major scientific databases. Extracted metrics included F1-Score, AUC-ROC, interpretability methods (SHAP/LIME), and cross-validation strategies, with statistical analyses using ANOVA and Pearson correlations. Results showed that XGBoost achieved the best average performance (F1-Score: 0.86; AUC-ROC: 0.84), although differences across algorithms were not statistically significant (
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.