Large language model powered knowledge graph construction for mental health exploration

作者：Shan Gao, Kaixian Yu, Yue Yang, Sheng Yu, Chenglong Shi, Xueqin Wang, Niansheng Tang, Hongtu Zhu · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-62781-z · 被引用次数：12 · 研究领域：Biomedical Text Mining and Ontologies、Machine Learning in Healthcare、Topic Modeling

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. Understanding the pathophysiological pathways of mental disorders and identifying reliable biomarkers remain challenging. This study introduces a large-scale knowledge graph tailored to mental disorders to improve knowledge discovery, disease prediction, and clinical validation