Advancing Alzheimer's research: A review of big data promises

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29 Scopus citations


Objective To review the current state of science using big data to advance Alzheimer's disease (AD) research and practice. In particular, we analyzed the types of research foci addressed, corresponding methods employed and study findings reported using big data in AD. Method Systematic review was conducted for articles published in PubMed from January 1, 2010 through December 31, 2015. Keywords with AD and big data analytics were used for literature retrieval. Articles were reviewed and included if they met the eligibility criteria. Results Thirty-eight articles were included in this review. They can be categorized into seven research foci: diagnosing AD or mild cognitive impairment (MCI) (n = 10), predicting MCI to AD conversion (n = 13), stratifying risks for AD (n = 5), mining the literature for knowledge discovery (n = 4), predicting AD progression (n = 2), describing clinical care for persons with AD (n = 3), and understanding the relationship between cognition and AD (n = 3). The most commonly used datasets are AD Neuroimaging Initiative (ADNI) (n = 16), electronic health records (EHR) (n = 11), MEDLINE (n = 3), and other research datasets (n = 8). Logistic regression (n = 9) and support vector machine (n = 8) are the most used methods for data analysis. Conclusion Big data are increasingly used to address AD-related research questions. While existing research datasets are frequently used, other datasets such as EHR data provide a unique, yet under-utilized opportunity for advancing AD research.

Original languageEnglish (US)
Pages (from-to)48-56
Number of pages9
JournalInternational Journal of Medical Informatics
StatePublished - Oct 2017

Bibliographical note

Funding Information:
The data analysis and manuscript preparation were supported by the National Center for Complementary and Integrative Health of the National Institutes of Health (NIH) under award number R01AT009457, the National Institute of General Medical Sciences of the NIH award number R01GM120079, and the National Institute on Aging of the NIH award number R01AG043392. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Publisher Copyright:
© 2017 Elsevier B.V.


  • Alzheimer's disease
  • Alzheimer's disease neuroimaging initiative
  • Electronic health records
  • Healthcare big data
  • Healthcare data analytics


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