AI驱动的基于真实临床对话的心理健康障碍早期诊断
AI-Powered Early Diagnosis of Mental Health Disorders from Real-World Clinical Conversations
AI总结：
本研究利用553个真实临床访谈数据集，评估LLM和微调模型在心理健康筛查中的效果，实现超80%准确率，PTSD达89%准确率，证明AI可提升早期诊断能力。
摘要：
心理健康障碍仍然是全球范围内导致残疾的主要原因之一，然而，由于主观评估、临床资源有限以及污名化和低认知度，抑郁症、焦虑症和创伤后应激障碍（PTSD）等疾病经常被漏诊或误诊。在初级保健环境中，研究表明，提供者在超过60%的病例中错误识别抑郁症或焦虑症，这凸显了对可扩展、可访问且具有上下文感知能力的诊断工具的迫切需求，以支持早期检测和干预。在本研究中，我们使用一个包含553个真实世界半结构化访谈的独特数据集，评估了机器学习模型在心理健康筛查中的有效性，每个访谈都配有重度抑郁发作（MDE）、焦虑障碍和PTSD的金标准诊断。我们基准测试了多种模型类别，包括使用GPT-4.1 Mini和MetaLLaMA的零样本提示，以及使用低秩适配（LoRA）进行微调的RoBERTa模型。我们的模型在各类诊断中实现了超过80%的准确率，在PTSD上表现尤为强劲（准确率高达89%，召回率高达98%）。我们还发现，使用较短的上下文、聚焦的上下文片段可以提高召回率，这表明聚焦的叙事线索能增强检测敏感性。LoRA微调被证明既高效又有效，较低秩的配置（例如秩8和16）在各项评估指标上保持了有竞争力的性能。我们的结果表明，基于LLM的模型可以比传统的自我报告筛查工具提供实质性改进，为低门槛、AI驱动的早期诊断提供了一条途径。这项工作为将机器学习整合到现实世界的临床工作流程中奠定了基础，特别是在获得及时心理健康护理最受限的低资源或高污名化环境中。
英文摘要：
Mental health disorders remain among the leading cause of disability worldwide, yet conditions such as depression, anxiety, and Post-Traumatic Stress Disorder (PTSD) are frequently underdiagnosed or misdiagnosed due to subjective assessments, limited clinical resources, and stigma and low awareness. In primary care settings, studies show that providers misidentify depression or anxiety in over 60% of cases, highlighting the urgent need for scalable, accessible, and context-aware diagnostic tools that can support early detection and intervention. In this study, we evaluate the effectiveness of machine learning models for mental health screening using a unique dataset of 553 real-world, semistructured interviews, each paried with ground-truth diagnoses for major depressive episodes (MDE), anxiety disorders, and PTSD. We benchmark multiple model classes, including zero-shot prompting with GPT-4.1 Mini and MetaLLaMA, as well as fine-tuned RoBERTa models using LowRank Adaptation (LoRA). Our models achieve over 80% accuracy across diagnostic categories, with especially strongperformance on PTSD (up to 89% accuracy and 98% recall). We also find that using shorter context, focused context segments improves recall, suggesting that focused narrative cues enhance detection sensitivity. LoRA fine-tuning proves both efficient and effective, with lower-rank configurations (e.g., rank 8 and 16) maintaining competitive performance across evaluation metrics. Our results demonstrate that LLM-based models can offer substantial improvements over traditional self-report screening tools, providing a path toward low-barrier, AI-powerd early diagnosis. This work lays the groundwork for integrating machine learning into real-world clinical workflows, particularly in low-resource or high-stigma environments where access to timely mental health care is most limited.
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。
