标题： 大型语言模型在心理健康诊断评估中的应用：探索大型语言模型在辅助心理健康诊断评估中的潜力——抑郁症和焦虑症案例 显示英文标题

标题： 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

摘要： 大型语言模型（LLMs）正日益引起医疗专业人员的关注，因为它们有望在诊断评估中提供帮助，这可能缓解由于患者数量多和医护人员短缺而导致的医疗系统压力。 为了使LLMs在支持诊断评估方面有效，至关重要的是它们能够紧密复制临床医生使用的标准诊断程序。 在本文中，我们特别研究了用于主要抑郁症（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）问卷中描述的诊断评估过程。 我们研究了各种提示和微调技术，以指导专有和开源LLMs遵循这些过程，并评估LLM生成的诊断结果与专家验证的真实结果之间的一致性。 在微调方面，我们使用了Mentalllama和Llama模型，而在提示方面，我们实验了专有模型如GPT-3.5和GPT-4o，以及开源模型如llama-3.1-8b和mixtral-8x7b。 显示英文摘要

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.