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

Abstract

Objective To review the current state of science using big data to advance AD research and practice. In particular, we analyzed the types of research foci addressed and 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 six 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 Alzheimer’s Disease 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-tapped opportunity for advancing AD research.

Data‐driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records

“…This leads to poor generalizability of the results in the real-world. 3,8,9 In addition, extensive follow-up to study the risk factors for AD is needed, because of the long prodrome before the suspicion of diagnosis and reverse causality. Certain symptoms or diseases may be a consequence of AD, rather than a risk factor.…”

Advancing Pharmacy Service using Big Data – Are We Fully Utilising the Big Data’s Potential Yet?

“…This, in turn, has led to the rapid translation of the knowledge into discovery of biomarkers and the development of novel targeted therapies. [13,14] The analysis, collection and processing of large volume of structured and unstructured biomedical data and surveys collected by researchers and pharmaceutical industries will build a better predictive modelling that could help to accelerate the drug discovery process. This success is a result of multidimensional Big Data analysis, artificial intelligence and machine learning in determining novel drug structure to target a selected pathological condition.…”

“…This success is a result of multidimensional Big Data analysis, artificial intelligence and machine learning in determining novel drug structure to target a selected pathological condition. [13,14] In addition to accelerating drug discovery, Big Data can also accelerate drug repurposing. Drug repurposing is the re-positioning the available clinical drugs for new indications.…”

“…In real life, patients are often complicated by comorbidities or unexpected events such as missing dose and delay in drug administration. [14] The discrepancies between the RCT and real world conditions have warranted the need of BCT, which is a comprehensive analysis of the overall samples instead of a smallsample subjects from a large population like RCT. This means that the clinical decision made based on BCT will be more clinically relevant.…”

Big Data, Data Science and Emerging Analytic tools : Impact in social science

“…In this regard, big data has been identified as an invaluable resource by the AD research community for usage in the development of innovative technogies aiding AD research. There has been several primary foci of research including diagnosing AD or mild cognitive impairment (MCI), predicting MCI to AD conversion, stratifying risks for AD, mining the literature for knowledge discovery, predicting AD progression, describing clinical care for persons with AD, and understanding the relationship between cognition and AD[11] [12]. The datasets used for AD research include Alzheimer's Disease Neuroimaging Initiative (ADNI), electronic health records (EHR), MEDLINE, and other research datasets.…”