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在真实世界数据中识别与阿尔茨海默病及相关痴呆症相关因素的可行性

Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data.

作者信息

Chen Aokun, Li Qian, Huang Yu, Li Yongqiu, Chuang Yu-Neng, Hu Xia, Guo Serena, Wu Yonghui, Guo Yi, Bian Jiang

机构信息

Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 1889 Museum Rd, Suite 7000, Gainesville, FL 32610.

Department of Computer Science, George R. Brown School of Engineering, Rice University, 6100 Main St., Houston, TX 77005.

出版信息

medRxiv. 2024 Feb 13:2024.02.10.24302621. doi: 10.1101/2024.02.10.24302621.

Abstract

A comprehensive view of factors associated with AD/ADRD will significantly aid in studies to develop new treatments for AD/ADRD and identify high-risk populations and patients for prevention efforts. In our study, we summarized the risk factors for AD/ADRD by reviewing existing meta-analyses and review articles on risk and preventive factors for AD/ADRD. In total, we extracted 477 risk factors in 10 categories from 537 studies. We constructed an interactive knowledge map to disseminate our study results. Most of the risk factors are accessible from structured Electronic Health Records (EHRs), and clinical narratives show promise as information sources. However, evaluating genomic risk factors using RWD remains a challenge, as genetic testing for AD/ADRD is still not a common practice and is poorly documented in both structured and unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk factors, literature mining via NLP methods offers a solution to automatically update our knowledge map.

摘要

对与阿尔茨海默病/阿尔茨海默病相关痴呆症(AD/ADRD)相关因素的全面了解将极大地有助于开展研究,以开发针对AD/ADRD的新疗法,并识别高风险人群和患者以进行预防工作。在我们的研究中,我们通过回顾现有的关于AD/ADRD风险和预防因素的荟萃分析及综述文章,总结了AD/ADRD的风险因素。我们总共从537项研究中提取了10个类别的477个风险因素。我们构建了一个交互式知识图谱来传播我们的研究结果。大多数风险因素可从结构化电子健康记录(EHR)中获取,临床叙述作为信息来源也显示出前景。然而,使用真实世界数据(RWD)评估基因组风险因素仍然是一项挑战,因为AD/ADRD的基因检测仍不常见,并且在结构化和非结构化EHR中记录都很少。考虑到关于AD/ADRD风险因素的研究不断发展,通过自然语言处理(NLP)方法进行文献挖掘为自动更新我们的知识图谱提供了一种解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7265/10889002/9b7e15fca839/nihpp-2024.02.10.24302621v1-f0001.jpg

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