用大模型结合DSM-5，自动分析心理论坛文本并生成个性化干预建议
MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM
- 基于DSM-5标准，结合时间信息与心理特征提取用户症状
- 可追踪症状演变过程，生成完整心理健康报告
- 适合临床医生、心理咨询师及企业员工关怀项目使用
心理论坛提供了关于心理问题、压力源及潜在解决方案的宝贵见解。我们提出MHINDR，一个基于大语言模型（LLM）的框架，整合DSM-5标准，分析用户生成文本，进行心理健康诊断，并为心理健康从业者生成个性化干预方案与洞察。该方法强调对时间信息的提取，以实现准确诊断与症状进展追踪，并结合心理特征构建用户全面的心理健康摘要。该框架提供可扩展、可定制、数据驱动的治疗建议，适用于多样化的临床场景、患者需求及职场福祉计划。
原文摘要 · Abstract (English)
Mental health forums offer valuable insights into psychological issues, stressors, and potential solutions. We propose MHINDR, a large language model (LLM) based framework integrated with DSM-5 criteria to analyze user-generated text, dignose mental health conditions, and generate personalized interventions and insights for mental health practitioners. Our approach emphasizes on the extraction of temporal information for accurate diagnosis and symptom progression tracking, together with psychological features to create comprehensive mental health summaries of users. The framework delivers scalable, customizable, and data-driven therapeutic recommendations, adaptable to diverse clinical contexts, patient needs, and workplace well-being programs.
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