用大模型挖掘社会因素对阿尔茨海默病的影响，构建可预测的新知识图谱。
Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs
- 用大语言模型自动提取文献中的社会因素与阿尔茨海默病关联信息
- 在增强的知识图谱上完成链接预测，验证了社会因素与生物因子的潜在联系
- 框架可推广至其他社会因素相关疾病研究，适合临床与健康政策学者
越来越多证据表明，社会决定因素（SDoH）——即非医疗类因素——会影响个体患阿尔茨海默病（AD）及相关痴呆症的风险。然而，这些关系背后的病因机制仍不清晰，主要因相关数据难以获取。本研究提出一种新型自动化框架，利用大语言模型（LLM）与自然语言处理技术，从海量文献中挖掘SDoH知识，并将其与从通用知识图谱PrimeKG中提取的AD相关生物实体进行整合。通过图神经网络执行链接预测任务，评估所构建的增强型知识图谱。结果表明，该框架在促进阿尔茨海默病知识发现方面具有潜力，且可推广至其他与社会决定因素相关的研究领域，为探索社会因素对健康结局的影响提供了新工具。代码已开源：https://github.com/hwq0726/SDoHenPKG。
原文摘要 · Abstract (English)
Growing evidence suggests that social determinants of health (SDoH), a set of nonmedical factors, affect individuals' risks of developing Alzheimer's disease (AD) and related dementias. Nevertheless, the etiological mechanisms underlying such relationships remain largely unclear, mainly due to difficulties in collecting relevant information. This study presents a novel, automated framework that leverages recent advancements of large language model (LLM) and natural language processing techniques to mine SDoH knowledge from extensive literature and integrate it with AD-related biological entities extracted from the general-purpose knowledge graph PrimeKG. Utilizing graph neural networks, we performed link prediction tasks to evaluate the resultant SDoH-augmented knowledge graph. Our framework shows promise for enhancing knowledge discovery in AD and can be generalized to other SDoH-related research areas, offering a new tool for exploring the impact of social determinants on health outcomes. Our code is available at: https://github.com/hwq0726/SDoHenPKG
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