| Title: | Large language model powered knowledge graph construction for mental health exploration |
| Journal: | Nature Communications |
| Published: | 13 Aug 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40804250/ |
| DOI: | https://doi.org/10.1038/s41467-025-62781-z |
Publication 19746
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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.</p>
6 Keywords
- Databases, Factual
- Humans
- Language
- Large Language Models
- Mental Disorders
- Mental Health
8 Authors
- Shan Gao
- Kaixian Yu
- Yue Yang
- Sheng Yu
- Chenglong Shi
- Xueqin Wang
- Niansheng Tang
- Hongtu Zhu
1 Application
| Application ID | Title |
|---|---|
| 98327 | Explore the possible relationship of brain diseases with combined genetic, functional genomic, clinical, and imaging data |
Enabling scientific discoveries that improve human health