Enhancing Depression Detection via Question-wise Modality Fusion
This work addresses the need for more efficient and interpretable automated depression diagnosis, which could reduce delays and human resource costs, though it appears incremental as it builds on existing multimodal approaches.
The paper tackled the problem of automating depression detection by addressing sub-optimal modality fusion and training methods in multimodal data, proposing a framework that achieves performance comparable to state-of-the-art models on the E-DAIC dataset and enhances interpretability for clinicians.
Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs. Current depression diagnosis involves determining the depression severity of a person through self-reported questionnaires or interviews conducted by clinicians. This often leads to delayed treatment and involves substantial human resources. Thus, several works try to automate the process using multimodal data. However, they usually overlook the following: i) The variable contribution of each modality for each question in the questionnaire and ii) Using ordinal classification for the task. This results in sub-optimal fusion and training methods. In this work, we propose a novel Question-wise Modality Fusion (QuestMF) framework trained with a novel Imbalanced Ordinal Log-Loss (ImbOLL) function to tackle these issues. The performance of our framework is comparable to the current state-of-the-art models on the E-DAIC dataset and enhances interpretability by predicting scores for each question. This will help clinicians identify an individual's symptoms, allowing them to customise their interventions accordingly. We also make the code for the QuestMF framework publicly available.