Large language model powered knowledge graph construction for mental health exploration

Shan Gao, Kaixian Yu, Yue Yang, Sheng Yu, Chenglong Shi, Xueqin Wang, Niansheng Tang () and Hongtu Zhu ()
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Shan Gao: Yunnan University
Kaixian Yu: Insilicom LLC
Yue Yang: University of North Carolina at Chapel Hill
Sheng Yu: Tsinghua University
Chenglong Shi: Kunming Medical University
Xueqin Wang: University of Science and Technology of China
Niansheng Tang: Yunnan University
Hongtu Zhu: University of North Carolina at Chapel Hill

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-62781-z

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