Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment

TL;DR

This paper presents 'Psycho Analyst', a specialized GPT-4-based model for mental health pre-screening that demonstrates high accuracy and detailed assessment capabilities, aiming to improve accessibility and support in mental health diagnostics.

Contribution

The study introduces a novel GPT-4-based model tailored for mental health screening, incorporating DSM-5 and PHQ-8 data, with a dual-task framework for nuanced assessment.

Findings

F1 score of 0.929 and Macro-F1 of 0.949 on DAIC-WOZ dataset

Lowest MAE of 2.89 and RMSE of 3.69 in PHQ-8 scoring

Enhanced accuracy and detailed scoring in mental health assessment

Abstract

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.