A gender-emotion interaction multi-task network for depression recognition via transformer-based multimodal fusion
-
By
-
Yujuan Xing
-
Ruifang He
-
Xiaoli Cao
-
Ping Tan
-
Li Chen
-
June 19, 2026
-
Clinical Scorecard: A Multi-Task Network Integrating Gender and Emotion for Recognizing Depression Through Transformer-Based Multimodal Fusion
At a Glance
| Category | Detail |
|---|
| Condition | Depression |
| Key Mechanisms | Gender-emotion interaction and transformer-based multimodal fusion |
| Target Population | Individuals with depression, particularly affected by gender and emotional factors |
| Care Setting | Automated depression recognition methods |
Key Highlights
- High prevalence and recurrence of depression significantly impact daily life.
- Speech-based features are effective for non-invasive depression detection.
- G-EIMTNet outperformed baseline models by 15.88% in accuracy and 14.73% in F1 score.
- Feature selection using MRMR enhances clinical interpretability.
- Gender and emotion significantly influence depression recognition.
Guideline-Based Recommendations
Diagnosis
- Current diagnosis relies on subjective reports and psychiatric evaluations.
Management
- Adoption of automated recognition methods for early intervention.
Monitoring & Follow-up
- Utilize speech analysis for ongoing assessment of depressive states.
Risks
- Depression is associated with increased risk of mortality.
Patient & Prescribing Data
Individuals experiencing depressive symptoms.
Non-invasive methods may increase treatment-seeking rates.
Clinical Best Practices
- Incorporate gender and emotional context in depression recognition models.
- Utilize multimodal approaches for enhanced feature extraction.
Related Resources & Content
