The content introduces a method utilizing a Graph Convolutional Network (GCN) for depression detection from transcribed clinical interviews. The proposed approach addresses the limitations of locality and self-connections in GCNs while achieving high accuracy. By evaluating on benchmark datasets, the method consistently outperforms previous models, achieving an F1 score of 0.84. The research highlights the importance of digital solutions in mental health diagnosis and treatment, emphasizing the power of language as an indicator of mental health status. Various neural network architectures have been explored in previous studies for depression detection, including sentiment-based approaches and hierarchical attention-based networks. The proposed GCN model stands out due to its simplicity, low computational cost, and interpretability capabilities. The study also delves into the experimental setup, implementation details, results analysis, and exploration of model interpretability through learned node embeddings.
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by Serg... lúc arxiv.org 03-12-2024
https://arxiv.org/pdf/2307.00920.pdfYêu cầu sâu hơn