Abstract

Depression is a widespread mental health disorder that adversely affects emotional well-being, cognitive functioning, and overall quality of life. Early and accurate detection is essential for timely intervention; however, traditional screening methods such as the Patient Health Questionnaire-9 (PHQ-9) are often limited by accessibility and resource constraints. To address these challenges, this study proposes a text-based automated depression screening system that predicts both PHQ-9 scores and depression severity levels from user-generated free-form text. The proposed approach utilizes DeBERTa-V3, a state-of-the-art transformer model, within a multi-task learning framework that simultaneously performs regression and multi-class classification. The model was trained on the PHQ-TextSet dataset, a synthetically constructed and PHQ-9-aligned corpus comprising 3,235 annotated samples across five severity categories. By leveraging disentangled attention and shared contextual representations, the system effectively captures nuanced linguistic and emotional patterns. Experimental results demonstrate high performance, achieving 99.85% classification accuracy, a macro F1-score of 0.9984, a weighted AUC of 0.96, and a Mean Absolute Error of 1.2495 for PHQ-9 score prediction. The model is deployed as a real-time inference system using FastAPI, highlighting its practical applicability in digital mental health platforms, telemedicine systems, and educational wellness tools.

Graphical Abstract

Novelty Statement

DeBERTa-V3 based multitask model using the PHQ-TextSet dataset predicts PHQ-9 scores and depression severity from text, enabling real-time, non-intrusive mental health screening.