Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment
This work addresses mental health assessment for the public and professionals, offering improved accessibility and cost-effectiveness, but it is incremental as it builds on existing GPT-4 technology.
This study tackled the problem of pre-screening mental health disorders by developing 'Psycho Analyst', a custom GPT model, which achieved an F1 score of 0.929 and a Macro-F1 score of 0.949 on the DAIC-WOZ dataset, with low error rates in PHQ-8 scoring.
This study introduces 'Psycho Analyst', a custom GPT model based on OpenAI's GPT-4, optimized for pre-screening mental health disorders. Enhanced with DSM-5, PHQ-8, detailed data descriptions, and extensive training data, the model adeptly decodes nuanced linguistic indicators of mental health disorders. It utilizes a dual-task framework that includes binary classification and a three-stage PHQ-8 score computation involving initial assessment, detailed breakdown, and independent assessment, showcasing refined analytic capabilities. Validation with the DAIC-WOZ dataset reveals F1 and Macro-F1 scores of 0.929 and 0.949, respectively, along with the lowest MAE and RMSE of 2.89 and 3.69 in PHQ-8 scoring. These results highlight the model's precision and transformative potential in enhancing public mental health support, improving accessibility, cost-effectiveness, and serving as a second opinion for professionals.