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Large language model powered knowledge graph construction for mental health exploration.

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Název: Large language model powered knowledge graph construction for mental health exploration.
Autoři: Gao, Shan, Yu, Kaixian, Yang, Yue, Yu, Sheng, Shi, Chenglong, Wang, Xueqin, Tang, Niansheng, Zhu, Hongtu
Zdroj: Nature Communications; 8/13/2025, Vol. 16 Issue 1, p1-16, 16p
Témata: MENTAL health, KNOWLEDGE graphs, MEDICAL needs assessment, EMPIRICAL research, MEDICAL literature, PREDICTION models, LANGUAGE models, MENTAL illness
Abstrakt: 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 [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt: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 [ABSTRACT FROM AUTHOR]
ISSN:20411723
DOI:10.1038/s41467-025-62781-z