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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.01614 |
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| _version_ | 1866916583234863104 |
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| author | Tang, Jinwen Shang, Yi |
| author_facet | Tang, Jinwen Shang, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01614 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment Tang, Jinwen Shang, Yi Computers and Society Artificial Intelligence 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. |
| title | Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2408.01614 |