用于心理健康诊断评估的大型语言模型：探索大型语言模型辅助心理健康诊断评估的潜力——抑郁症与焦虑症案例
Large Language Models for Mental Health Diagnostic Assessments: Exploring The Potential of Large Language Models for Assisting with Mental Health Diagnostic Assessments -- The Depression and Anxiety Case
AI总结：
本文研究利用LLMs辅助PHQ-9和GAD-7诊断评估，通过提示与微调技术使模型遵循标准诊断流程，并验证其与专家诊断的一致性。
摘要：
大型语言模型（LLMs）因其在辅助诊断评估方面的潜力而日益受到医疗保健专业人员的关注，这有望缓解因患者数量庞大和提供者短缺而给医疗系统带来的压力。要使LLMs在支持诊断评估方面发挥作用，关键在于它们必须严格复现临床医生使用的标准诊断程序。本文专门考察了用于重度抑郁障碍（MDD）的患者健康问卷-9（PHQ-9）和用于广泛性焦虑障碍（GAD）的广泛性焦虑障碍-7（GAD-7）问卷中所描述的诊断评估过程。我们研究了多种提示和微调技术，以引导专有和开源LLMs遵循这些过程，并评估了LLM生成的诊断结果与专家验证的真实标签之间的一致性。在微调方面，我们使用了Mentalllama和Llama模型；在提示方面，我们实验了GPT-3.5和GPT-4o等专有模型，以及llama-3.1-8b和mixtral-8x7b等开源模型。
英文摘要：
Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic assessments, which could alleviate the strain on the healthcare system caused by a high patient load and a shortage of providers. For LLMs to be effective in supporting diagnostic assessments, it is essential that they closely replicate the standard diagnostic procedures used by clinicians. In this paper, we specifically examine the diagnostic assessment processes described in the Patient Health Questionnaire-9 (PHQ-9) for major depressive disorder (MDD) and the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for generalized anxiety disorder (GAD). We investigate various prompting and fine-tuning techniques to guide both proprietary and open-source LLMs in adhering to these processes, and we evaluate the agreement between LLM-generated diagnostic outcomes and expert-validated ground truth. For fine-tuning, we utilize the Mentalllama and Llama models, while for prompting, we experiment with proprietary models like GPT-3.5 and GPT-4o, as well as open-source models such as llama-3.1-8b and mixtral-8x7b.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
