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

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.