المفاهيم الأساسية
The author proposes a novel approach using a Graph Convolutional Network to detect depression from transcribed clinical interviews, showcasing improved performance and interpretability.
الملخص
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.
الإحصائيات
Results show that our approach consistently outperforms the vanilla GCN model as well as previously reported results, achieving an F1=0.84 on both datasets.
Loss is computed by means of the cross-entropy function between Zi and Yi, ∀i ∈ Vtr docs.
For each partition, we divide the table into non-GCN models (i.e., classic and BERT-based baselines and previous research) and GCN models (vanilla GCN and our proposed ω-GCN).
On DAIC-WOZ dataset, ω-GCN obtains a macro F1 = 0.84 with only top-250 words.
On E-DAIC dataset, the ω-GCN obtains the best performance among considered methods with a macro-F1 of 0.80 and 0.84 for dev and test partitions respectively.
اقتباسات
"The proposed method aims to mitigate the limiting assumptions of locality and equal importance of self-connections vs edges."
"Our best configurations require orders of magnitude fewer trainable parameters than transformer-based models."
"The proposed approach has some attractive features including a simple yet novel weighting approach for self-connection edges."