In the next 30 years, Alzheimer’s disease cases are predicted to drastically increase. Consequently, there is a critical need for research that can counteract the increasing number of Alzheimer’s disease patients. However, current methods of Alzheimer’s disease research have significant limitations. For example, Alzheimer’s disease research is often restricted by resource, temporal, and recruitment barriers (e.g., participant dropout). Unlike standard research, big data analysis is excellent at investigating complex long-term phenomena such as Alzheimer’s disease. Big data methods can also overcome many of the limitations that restrict Alzheimer’s disease research. Accordingly, researchers are turning to big data methods to study Alzheimer’s disease. In this chapter, we outline the applications of big data to Alzheimer’s disease research as well as common methods used to collect and analyze big data. We also explore how big data research could be used to treat, diagnose, and understand Alzheimer’s disease. Accordingly, we aim to provide a general understanding of big data methods in Alzheimer’s disease research and highlight the advantages of big data analysis over standard dementia research.
Elsevier, Alzheimer’s Disease: Understanding Biomarkers, Big Data, and Therapy, Volume , 1 January 2021