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

This study introduces ’Psycho Analyst’, a custom GPT model based on OpenAI’s GPT-4, optimized for prescreening 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.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/cogmi62246.2024.00030
Citations
7
Primary Topic
Mental Health Treatment and Access
Type
article
Field-Weighted Citation Impact
4.50
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article

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

Jing‐Shia Tang, Yi Shang
7 citations
Mental Health Treatment and Access
4.50
article

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

Jing‐Shia Tang, Yi Shang
article en
7 citations

Abstract

This study introduces ’Psycho Analyst’, a custom GPT model based on OpenAI’s GPT-4, optimized for prescreening 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.

University of Missouri (US)
Reduced inequalities
Openalex Percentile: Top 5%
Mental Health Treatment and Access
4.50
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