利用LLM增强的文献挖掘和知识图谱在阿尔茨海默病研究中整合健康社会决定因素
Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs
AI总结：
针对健康社会决定因素（SDoH）与阿尔茨海默病（AD）关系机制不明的问题，本研究提出LLM增强的文献挖掘与知识图谱整合框架，通过挖掘SDoH知识并与PrimeKG的AD生物实体结合，经图神经网络链接预测验证，为AD及其他SDoH相关研究提供知识发现新工具。
摘要：
越来越多的证据表明，健康社会决定因素（SDoH）这组非医疗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，这类关系背后的病因机制仍大多不明确，主要因收集相关信息存在困难。本研究提出一种新颖的自动化框架，利用大型语言模型（LLM）和自然语言处理技术的最新进展，从大量文献中挖掘SDoH知识，并将其与从通用知识图谱PrimeKG提取的AD相关生物实体整合。通过图神经网络，我们执行链接预测任务评估生成的SDoH增强知识图谱。该框架在促进AD知识发现方面显示出潜力，且可推广至其他SDoH相关研究领域，为探索社会决定因素对健康结果的影响提供新工具。代码可在https://github.com/hwq0726/SDoHenPKG获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
