ArticleiScience2026
Ensemble transformer with post-hoc explanations for depression emotion and severity detection.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Integrated design of an efficient multi spectral imaging and federated learning framework for precision crop disease diagnosis in low-resource farming communities.Scientific reports · 2026Article
- Domain-robust vision transformer with hierarchical swin encoding for explainable low-latency driver drowsiness detection.Scientific reports · 2026Article
- An Artificial Intelligence-Based Detection of Comorbid Depression, Anxiety, and Substance Use Disorder in Korean Counseling Dialogues Using an Explainable Hierarchical Attention Network with Shapley Additive Explanations.Diagnostics (Basel, Switzerland) · 2026Article
- Genetic determinants of metabolic-inflammatory dysregulation and machine learning prediction of COVID-19.Frontiers in cellular and infection microbiology · 2026Article
- Article
- Explainable AI-driven hybrid deep learning framework for accurate skin cancer diagnosis.Digital healthArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents an ensemble transformer framework for detecting depression-related emotions and classifying their severity in social media text. It addresses the need for scalable and trustworthy AI solutions in mental health by integrating four transformer models. The DepTformer-XAI-SV model uses a weighted soft-voting mechanism based on validation macro-F1 scores to improve accuracy and incorporates LIME to highlight key linguistic features associated with depression. The framework is evaluated on two benchmark datasets: DepressionEmo, with eight emotion classes, and the merged depression severity detection (MDSD), with four severity levels, both sourced from social media. To address class imbalance, we use class-weighted cross-entropy, stratified k-fold splits, and minority-aware sampling. Results show that the model surpasses individual transformer models and traditional methods, achieving macro-F1 scores of 80.44% for DepressionEmo and 79.88% for MDSD, significantly improving minority class detection. Lastly, a web application has been developed for interactive and interpretable inference.
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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.