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

Author:

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

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Applications (stat.AP)

journal:

date:

2024-02-03 00:00:00

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.

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