Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework

作者：Xu Zhang, Chenlong Li, Weisi Chen, Jiaxin Zheng, Feihong Li · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-90117-w · 被引用次数：19 · 研究领域：Mental Health via Writing、Sentiment Analysis and Opinion Mining、Emotion and Mood Recognition

In recent years, the number of people suffering from depression has gradually increased, and early detection is of great significance for the well-being of the public. However, the current methods for detecting depression are relatively limited, typically relying on the self-rating depression scale (SDS) and interviews. These methods are influenced by subjective or environmental factors. To improve the objectivity and efficiency of diagnosis, deep learning techniques have been applied to the field of automatic depression detection (ADD), providing a more accurate and objective approach. During interviews, transcribed interview data is one of the most commonly used modalities in ADD. However, previous studies have only utilized response texts or selected question-answer pairs, resulting in information redundancy and loss. This paper is the first to apply the multiple instance learning (MIL) framework to the field of textual interview data, aiming to overcome issues of inadequate text representation and ineffective information extraction in long texts. In the MIL framework, each instance undergoes an independent feature extraction process, ensuring that the local features of each instance are fully captured. This not only enhances the overall text representation capability but also alleviates the issue of sample imbalance in the dataset. Additionally, this paper improves upon previous aggregation strategies by introducing two hyper-parameters to accommodate the uncertainties in...