Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-05499-8 |
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| author | Zihan Zhou Ziyi Zeng Wenhao Jiang Yihui Zhu Jiaxin Mao Yonggui Yuan Min Xia Shubin Zhao Mengyu Yao Yunqian Chen |
| author_facet | Zihan Zhou Ziyi Zeng Wenhao Jiang Yihui Zhu Jiaxin Mao Yonggui Yuan Min Xia Shubin Zhao Mengyu Yao Yunqian Chen |
| author_sort | Zihan Zhou |
| collection | DOAJ |
| description | Abstract 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 proximity scores, 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. |
| format | Article |
| id | doaj-art-9a6c3a334f834f7da4e5c8a3aa61429d |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9a6c3a334f834f7da4e5c8a3aa61429d2025-08-20T03:05:22ZengNature PortfolioScientific Reports2045-23222025-07-0115112310.1038/s41598-025-05499-8Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language modelsZihan Zhou0Ziyi Zeng1Wenhao Jiang2Yihui Zhu3Jiaxin Mao4Yonggui Yuan5Min Xia6Shubin Zhao7Mengyu Yao8Yunqian Chen9School of Automation, Nanjing University of Information Science and TechnologySchool of Automation, Nanjing University of Information Science and TechnologyDepartment of Psychiatry and Psychosomatics, School of Medicine, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Zhongda Hospital, Southeast UniversitySchool of Automation, Nanjing University of Information Science and TechnologyThe First Affiliated Hospital of Ningbo UniversityDepartment of Psychiatry and Psychosomatics, School of Medicine, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, Zhongda Hospital, Southeast UniversitySchool of Automation, Nanjing University of Information Science and TechnologyNanjing LES Information Technology Co., Ltd.School of Automation, Nanjing University of Information Science and TechnologySchool of Automation, Nanjing University of Information Science and TechnologyAbstract 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 proximity scores, 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.https://doi.org/10.1038/s41598-025-05499-8Psychological diseaseGraph structure analysisNetwork distanceProximity metricClinical manifestation |
| spellingShingle | Zihan Zhou Ziyi Zeng Wenhao Jiang Yihui Zhu Jiaxin Mao Yonggui Yuan Min Xia Shubin Zhao Mengyu Yao Yunqian Chen Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models Scientific Reports Psychological disease Graph structure analysis Network distance Proximity metric Clinical manifestation |
| title | Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models |
| title_full | Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models |
| title_fullStr | Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models |
| title_full_unstemmed | Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models |
| title_short | Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models |
| title_sort | research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models |
| topic | Psychological disease Graph structure analysis Network distance Proximity metric Clinical manifestation |
| url | https://doi.org/10.1038/s41598-025-05499-8 |
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