Predicting depression by using a novel deep learning model and video-audio-text multimodal data
Bibliographic Data
| ID | 15528987 |
|---|---|
| Authors | Yifu Li (0000-0003-0602-8429, Northeastern University), Xueping Yang (0000-0002-9184-8116, Liaoning Provincial People's Hospital), Meng Zhao (0000-0001-7199-272X, Northeastern University), Jiangtao Wang (0009-0000-2562-0888, Northeastern University), Yudong Yao (0000-0003-3868-0593, Stevens Institute of Technology), Wei Qian (0000-0003-4775-550X), Qian Wei (0000-0002-2459-5577, Northeastern University), Shouliang Qi (0000-0003-0977-1939, Northeastern University, corresponding author) |
| Year | 2025 |
| Volume | 16 |
| Pages | 1602650-1602650 |
| Publication date | 2025-09-24 |
| Peer Reviewed | Yes |
| 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.2025.1602650 |
| PMID | 41069943 |
| OpenAlex | W4414480338 |
| Language | EN |
| References cited | 53 |
These results underscore the robustness and precision of the IMDD-Net, highlighting the importance of integrating local and global features across multiple modalities for accurate depression prediction
Deep learning · Depression (economics · Major depressive disorder · Modalities · Multimodal therapy · Robustness (evolution · Digital Mental Health Interventions · Emotion and Mood Recognition · Mental Health via Writing
| Citation velocity | historical |
|---|---|
| Highly cited | No |