Enhancing early depression detection with AI: a comparative use of NLP models

Abstract

One of the most underdiagnosed medical conditions worldwide is depression. It has been demonstrated that the current classical procedures for early detection of depression are insufficient, which emphasizes the importance of seeking a more efficient approach to overcome this challenge. One of the most promising opportunities is arising in the field of Artificial Intelligence as AI-based models could have the capacity to offer a fast, widely accessible, unbiased and efficient method to address this problem. In this paper, we compared three natural language processing models, namely, BERT, GPT-3.5 and GPT-4 on three different datasets. Our findings show that different levels of efficacy are shown by fine-tuned BERT, GPT-3.5, and GPT-4 in identifying depression from textual data. By comparing the models on the metrics such as accuracy, precision, and recall, our results have shown that GPT-4 outperforms both BERT and GPT-3.5 models, even without previous fine-tuning, showcasing its enormous potential to be utilized for automated depression detection on textual data. In the paper, we present newly introduced datasets, fine-tuning and model testing processes, while also addressing limitations and discussing further considerations for future research.