Large language model powered knowledge graph construction for mental health exploration
Abstract Mental health is a major global concern, yet findings remain fragmented across studies and databases, hindering integrative understanding and clinical translation. To address this gap, we present the Mental Disorders Knowledge Graph (MDKG)—a large-scale, contextualized knowledge graph built...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62781-z |
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| _version_ | 1849332570915864576 |
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| author | Shan Gao Kaixian Yu Yue Yang Sheng Yu Chenglong Shi Xueqin Wang Niansheng Tang Hongtu Zhu |
| author_facet | Shan Gao Kaixian Yu Yue Yang Sheng Yu Chenglong Shi Xueqin Wang Niansheng Tang Hongtu Zhu |
| author_sort | Shan Gao |
| collection | DOAJ |
| description | Abstract Mental health is a major global concern, yet findings remain fragmented across studies and databases, hindering integrative understanding and clinical translation. To address this gap, we present the Mental Disorders Knowledge Graph (MDKG)—a large-scale, contextualized knowledge graph built using large language models to unify evidence from biomedical literature and curated databases. MDKG comprises over 10 million relations, including nearly 1 million novel associations absent from existing resources. By structurally encoding contextual features such as conditionality, demographic factors, and co-occurring clinical attributes, the graph enables more nuanced interpretation and rapid expert validation, reducing evaluation time by up to 70%. Applied to predictive modeling in the UK Biobank, MDKG-enhanced representations yielded significant gains in predictive performance across multiple mental disorders. As a scalable and semantically enriched resource, MDKG offers a powerful foundation for accelerating psychiatric research and enabling interpretable, data-driven clinical insights. |
| format | Article |
| id | doaj-art-d4a6970b64b245b0b6df08111ffd820c |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d4a6970b64b245b0b6df08111ffd820c2025-08-20T03:46:09ZengNature PortfolioNature Communications2041-17232025-08-0116111610.1038/s41467-025-62781-zLarge language model powered knowledge graph construction for mental health explorationShan Gao0Kaixian Yu1Yue Yang2Sheng Yu3Chenglong Shi4Xueqin Wang5Niansheng Tang6Hongtu Zhu7Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan UniversityInsilicom LLCDepartment of Biostatistics, University of North Carolina at Chapel HillDepartment of Statistics and Data Science, Tsinghua UniversityThe Second Affiliated Hospital of Kunming Medical University, Kunming Medical UniversitySchool of Management, University of Science and Technology of ChinaYunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan UniversityDepartment of Biostatistics, University of North Carolina at Chapel HillAbstract Mental health is a major global concern, yet findings remain fragmented across studies and databases, hindering integrative understanding and clinical translation. To address this gap, we present the Mental Disorders Knowledge Graph (MDKG)—a large-scale, contextualized knowledge graph built using large language models to unify evidence from biomedical literature and curated databases. MDKG comprises over 10 million relations, including nearly 1 million novel associations absent from existing resources. By structurally encoding contextual features such as conditionality, demographic factors, and co-occurring clinical attributes, the graph enables more nuanced interpretation and rapid expert validation, reducing evaluation time by up to 70%. Applied to predictive modeling in the UK Biobank, MDKG-enhanced representations yielded significant gains in predictive performance across multiple mental disorders. As a scalable and semantically enriched resource, MDKG offers a powerful foundation for accelerating psychiatric research and enabling interpretable, data-driven clinical insights.https://doi.org/10.1038/s41467-025-62781-z |
| spellingShingle | Shan Gao Kaixian Yu Yue Yang Sheng Yu Chenglong Shi Xueqin Wang Niansheng Tang Hongtu Zhu Large language model powered knowledge graph construction for mental health exploration Nature Communications |
| title | Large language model powered knowledge graph construction for mental health exploration |
| title_full | Large language model powered knowledge graph construction for mental health exploration |
| title_fullStr | Large language model powered knowledge graph construction for mental health exploration |
| title_full_unstemmed | Large language model powered knowledge graph construction for mental health exploration |
| title_short | Large language model powered knowledge graph construction for mental health exploration |
| title_sort | large language model powered knowledge graph construction for mental health exploration |
| url | https://doi.org/10.1038/s41467-025-62781-z |
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