Sentence-level multi-modal feature learning for depression recognition
Bibliographic Data
| ID | 15517015 |
|---|---|
| Authors | Guanghua Zhang (0000-0003-1260-5245, Taiyuan Normal University, corresponding author), Guangping Zhuo (0000-0001-7657-6071, Taiyuan Normal University), Yang Yang (0000-0002-3705-7612, Taiyuan Heavy Industry (China)), Guohua Xu (0000-0002-3283-2392), Augix Guohua Xu (0000-0002-8509-1137, Taiyuan Heavy Industry (China)), Shukui Ma (0009-0002-9535-2211, Taiyuan Normal University), Hao Liu (0000-0002-4971-7315, Taiyuan Normal University), Zhiyan Ren (0009-0006-6267-4440, corresponding author), Zhiyong Ren |
| Year | 2025 |
| Volume | 16 |
| Pages | 1439577-1439577 |
| Publication date | 2025-03-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2025.1439577 |
| PMID | 40191110 |
| OpenAlex | W4408717571 |
| Language | EN |
| References cited | 21 |
The SMFL methodology represents a significant advancement in the diagnostic processes of depression, promising a cost-effective, private, and accessible diagnostic tool that aligns with the PHQ-8 clinical standard. By broadening the accessibility of mental health resources, this methodology has the potential to revolutionize the landscape of psychiatric evaluation, augmenting the precision of depression identification and enhancing the overall mental health management infrastructure
Convolutional neural network · Depression (economics · Feature (linguistics · Feature learning · Linguistics · Machine learning · Modal · Modalities · Modality (human–computer interaction · Representation (politics · Sentence · Computer Science · Emotion and Mood Recognition · Mental Health Treatment and Access · Mental Health via Writing · Artificial Intelligence
| Highly cited | No |
|---|