Name
Title | ||
|---|---|---|
What reveals about depression level? The role of multimodal features at the level of interview questions |
Abstract | ||
|---|---|---|
Early depression detection can enable timely intervention. Automatic depression detection has relied on features extracted from individual-level data, which may be too coarse to support effective detection. Existing detection models have largely overlooked interview questions commonly used in clinical depression assessment. This research proposes a two-layered multi-modal model for depression detection, which not only extracts features from responses at a level of individual interview questions, but also identifies semantic categories of those questions. The evaluation results demonstrate that the proposed model outperforms the state-of-the-art methods for depression detection. The research findings have broad and cross-disciplinary implications. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.im.2020.103349 | Information & Management |
Keywords | DocType | Volume |
Depression detection,Interview question,Multimodal feature,Sensitivity analysis,Question category | Journal | 57 |
Issue | ISSN | Citations |
7 | 0378-7206 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shan Guohou | 1 | 0 | 0.34 |
| Zhou Lina | 2 | 2 | 0.71 |
| Dongsong Zhang | 3 | 1599 | 99.71 |