{"title":"推进心理健康预检:用于心理压力评估的新定制 GPT","authors":"Jinwen Tang, Yi Shang","doi":"arxiv-2408.01614","DOIUrl":null,"url":null,"abstract":"This study introduces 'Psycho Analyst', a custom GPT model based on OpenAI's\nGPT-4, optimized for pre-screening mental health disorders. Enhanced with\nDSM-5, PHQ-8, detailed data descriptions, and extensive training data, the\nmodel adeptly decodes nuanced linguistic indicators of mental health disorders.\nIt utilizes a dual-task framework that includes binary classification and a\nthree-stage PHQ-8 score computation involving initial assessment, detailed\nbreakdown, and independent assessment, showcasing refined analytic\ncapabilities. Validation with the DAIC-WOZ dataset reveals F1 and Macro-F1\nscores of 0.929 and 0.949, respectively, along with the lowest MAE and RMSE of\n2.89 and 3.69 in PHQ-8 scoring. These results highlight the model's precision\nand transformative potential in enhancing public mental health support,\nimproving accessibility, cost-effectiveness, and serving as a second opinion\nfor professionals.","PeriodicalId":501112,"journal":{"name":"arXiv - CS - Computers and Society","volume":"108 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment\",\"authors\":\"Jinwen Tang, Yi Shang\",\"doi\":\"arxiv-2408.01614\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study introduces 'Psycho Analyst', a custom GPT model based on OpenAI's\\nGPT-4, optimized for pre-screening mental health disorders. Enhanced with\\nDSM-5, PHQ-8, detailed data descriptions, and extensive training data, the\\nmodel adeptly decodes nuanced linguistic indicators of mental health disorders.\\nIt utilizes a dual-task framework that includes binary classification and a\\nthree-stage PHQ-8 score computation involving initial assessment, detailed\\nbreakdown, and independent assessment, showcasing refined analytic\\ncapabilities. Validation with the DAIC-WOZ dataset reveals F1 and Macro-F1\\nscores of 0.929 and 0.949, respectively, along with the lowest MAE and RMSE of\\n2.89 and 3.69 in PHQ-8 scoring. These results highlight the model's precision\\nand transformative potential in enhancing public mental health support,\\nimproving accessibility, cost-effectiveness, and serving as a second opinion\\nfor professionals.\",\"PeriodicalId\":501112,\"journal\":{\"name\":\"arXiv - CS - Computers and Society\",\"volume\":\"108 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Computers and Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.01614\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computers and Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.01614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 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.