Enhancing AI depression detection using transfer learning

Depression is a serious mental health disorder that poses significant challenges to individuals’ emotional well-being, daily functioning, and overall quality of life. While artificial intelligence methods offer promising solutions in diagnosing, their development typically requires large datasets, which are difficult due to privacy concerns and the complex nature of clinical diagnosis. To address these challenges, this study leverages transfer learning to improve depression detection. Specifically, the method employs the bidirectional encoder representations from transformers (BERT) pre-trained model on large-scale language data, and fine-tunes it on the Extended Distress Analysis Interview Corpus to adapt the model for depression detection. By using the BERT Tokenizer, interview transcripts were tokenized to retain critical linguistic context. These tokens were then processed by the pre-trained bert-base-uncased model to extract robust language features. The features were then passed through a 1-dimensional convolutional neural network (1DCNN) for further analysis, enabling the detection of depression-related patterns. Finally, the refined features were classified via dense layers. To investigate the effectiveness of this method, a total of 11 models-six conventional machine learning models and five neural networks-were tested using three tokenization methods for comparison. Among them, the BERT Tokenizer + 1DCNN model achieved the best performance, with an accuracy of 89.3%, F1 score of 89.4%, and AUC of 95.0%. Notably, transfer learning improved accuracy by 7.7%, highlighting its effectiveness in training neural networks on small datasets. These results demonstrate that the proposed approach not only addresses the issue of limited training data but also significantly enhances the accuracy and reliability of AI-aided depression detection. The method can be applied in computer-aided systems for improving clinical depression diagnostics and other healthcare applications where data scarcity is a barrier.

Text 123479.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) Official URL or Download Paper: https://ojs.wiserpub.com/index.php/CM/article/view...

Actions (login required)

View Item