用大模型构建心理疾病知识图谱，发现病症间距离越近越相似。
Research on the Proximity Relationships of Psychosomatic Disease Knowledge Graph Modules Extracted by Large Language Models
- 基于BERT与微调LLM识别实体，构建含9668条三元组的知识图谱。
- 病症间网络距离越近，临床表现、治疗方式和心理机制越相似。
- 主诊断关系中症状-疾病对的关联更强，更适合作为诊断参考。
随着社会变迁加速，心身障碍发病率显著上升，成为全球健康重大挑战。本文建立本体模型与实体类型，利用BERT及LoRA微调的大语言模型进行命名实体识别，构建包含9668条三元组的知识图谱。通过分析疾病、症状、药物模块间的网络距离，发现病症间距离越近，其临床表现、治疗策略及心理机制越相似；症状间距离越近，越可能共现。比较网络距离d与临近度z分数显示，主诊断关系中的症状-疾病对关联性更强，参考价值更高。研究揭示了疾病间潜在联系、共现症状及治疗策略相似性，为心身障碍的诊疗提供新视角，并为未来心理健康研究与实践提供重要信息。
原文摘要 · Abstract (English)
As social changes accelerate, the incidence of psychosomatic disorders has significantly increased, becoming a major challenge in global health issues. This necessitates an innovative knowledge system and analytical methods to aid in diagnosis and treatment. Here, we establish the ontology model and entity types, using the BERT model and LoRA-tuned LLM for named entity recognition, constructing the knowledge graph with 9668 triples. Next, by analyzing the network distances between disease, symptom, and drug modules, it was found that closer network distances among diseases can predict greater similarities in their clinical manifestations, treatment approaches, and psychological mechanisms, and closer distances between symptoms indicate that they are more likely to co-occur. Lastly, by comparing the proximity d and proximity z score, it was shown that symptom-disease pairs in primary diagnostic relationships have a stronger association and are of higher referential value than those in diagnostic relationships. The research results revealed the potential connections between diseases, co-occurring symptoms, and similarities in treatment strategies, providing new perspectives for the diagnosis and treatment of psychosomatic disorders and valuable information for future mental health research and practice.
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