用知识图谱引导大模型，让自由文本中的压力信息变结构化。
Feasibility of Structuring Stress Documentation Using an Ontology-Guided Large Language Model
- 基于压力理论构建知识图谱，指导大模型提取关键信息。
- 从35篇帖子中提取220项信息，准确率达78.2%。
- 适合临床智能记录、心理健康研究者参考。
压力源于外部刺激、个体评估与生理心理反应的动态交互，显著影响健康但常被低估且文档不规范，多以电子病历中的自由文本形式记录。环境人工智能技术有望减轻记录负担，但生成内容仍多为非结构化叙述，限制临床应用。本研究旨在构建精神压力知识图谱（MeSO），并评估大模型（LLM）基于该图谱从叙述性文本中提取结构化信息的可行性。MeSO结合了压力的交易模型及11种已验证评估工具的概念，经知识图谱漏洞检测工具和专家验证优化。利用MeSO，从35条Reddit帖子中提取六类信息：压力源、压力反应、应对策略、持续时间、起始时间与时间模式。人类评审员评估准确性与知识图谱覆盖度。最终知识图谱包含8个顶层类别下的181个概念。在可提取的220项压力相关条目中，大模型正确识别172项（78.2%），误分类27项（12.3%），遗漏21项（9.5%）。所有正确提取项均准确映射至MeSO，但有24个相关概念尚未纳入图谱。结果表明，基于知识图谱引导的大模型可有效实现压力信息的结构化提取，有助于提升环境人工智能系统中压力记录的一致性与实用性。未来工作应拓展至临床对话数据，并比较不同大模型表现。
原文摘要 · Abstract (English)
Stress, arising from the dynamic interaction between external stressors, individual appraisals, and physiological or psychological responses, significantly impacts health yet is often underreported and inconsistently documented, typically captured as unstructured free-text in electronic health records. Ambient AI technologies offer promise in reducing documentation burden, but predominantly generate unstructured narratives, limiting downstream clinical utility. This study aimed to develop an ontology for mental stress and evaluate the feasibility of using a Large Language Model (LLM) to extract ontology-guided stress-related information from narrative text. The Mental Stress Ontology (MeSO) was developed by integrating theoretical models like the Transactional Model of Stress with concepts from 11 validated stress assessment tools. MeSO's structure and content were refined using Ontology Pitfall Scanner! and expert validation. Using MeSO, six categories of stress-related information--stressor, stress response, coping strategy, duration, onset, and temporal profile--were extracted from 35 Reddit posts using Claude Sonnet 4. Human reviewers evaluated accuracy and ontology coverage. The final ontology included 181 concepts across eight top-level classes. Of 220 extractable stress-related items, the LLM correctly identified 172 (78.2%), misclassified 27 (12.3%), and missed 21 (9.5%). All correctly extracted items were accurately mapped to MeSO, although 24 relevant concepts were not yet represented in the ontology. This study demonstrates the feasibility of using an ontology-guided LLM for structured extraction of stress-related information, offering potential to enhance the consistency and utility of stress documentation in ambient AI systems. Future work should involve clinical dialogue data and comparison across LLMs.
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