• Weiqi Wang and Eswar Krishnan.
• School of Medicine, Stanford University, Palo Alto, CA, United States.
• JMIR Med Inform. 2014 Jan 1;2(1):e1.

BackgroundIn the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality.ObjectiveThe objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm.MethodsWe conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years.ResultsThis paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data.ConclusionsBig data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data.

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