A deep learning-based model for detecting depression in senior population
Bibliographic Data
| ID | 15529685 |
|---|---|
| Authors | Yunhan Lin (0000-0003-2779-2637, Peking University), Biman Najika Liyanage (0009-0004-7123-4194), Yutao Sun (0000-0002-8198-5387), Tianlan Lu (Chinese Academy of Medical Sciences & Peking Union Medical College), Zhengwen Zhu, Yundan Liao (0000-0002-0969-6927, Chinese Academy of Medical Sciences & Peking Union Medical College), Qiushi Wang (0000-0001-5196-5011), CHUAN SHI (0000-0002-2627-1189, Peking University), Weihua Yue (0000-0002-1201-8465, Chinese Institute for Brain Research, corresponding author) |
| Year | 2022 |
| Volume | 13 |
| Pages | 1016676-1016676 |
| Publication date | 2022-11-07 |
| 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.2022.1016676 |
| PMID | 36419976 |
| OpenAlex | W4308472677 |
| Language | EN |
| References cited | 4 |
This study provides a new method for rapid identification and diagnosis of depression utilizing deep learning technology. Vocal biomarkers extracted from raw speech signals have high potential for the early diagnosis of depression in older adults
Anxiety · Audiology · Binary classification · Deep learning · Depression (economics · Machine learning · Major depressive disorder · Mandarin Chinese · Mini-international neuropsychiatric interview · Population · Psychiatry · Raw data · Raw score · Receiver operating characteristic · Statistics · Computer Science · Emotion and Mood Recognition · Mathematics · Medicine · Mental Health via Writing · Psychology · Voice and Speech Disorders · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |