What reveals about depression level? The role of multimodal features at the level of interview questions
Authors: Guohou, Shan; Lina, Zhou; Dongsong, Zhang
Journal: Information & Management (2020)
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 efective 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.