A review of the literature on big data analytics in healthcare

Abstract

Big data analytics (BDA) is of paramount importance in healthcare aspects such as patient diagnostics, fast epidemic recognition, and improvement of patient management. The objective of this profiling study is (a) to provide an overview of the BDA publication dynamics in the healthcare domain and (b) to discuss this scientific field through related examples. A sampling literature review has been conducted. A total of 804 papers have been identified and content analysis has been performed to mine knowledge in the domain for the years 2000–2016. The findings show that co-authors’ backgrounds are from the subject areas of medicine and computer sciences. Most articles are experimental in nature and use modeling and machine learning techniques to exploit clinical data, for health monitoring and prediction purposes. Many articles are relevant to the medical specialties of neurology/neurosurgery/neuropsychiatry, medical oncology, and cardiology. Well-cited papers investigate the identification and management of high-risk/cost patients, the use of big data, Hadoop and cloud computing in genomics, and the development of mobile applications for disease management. Important is also the research about improving disease prediction by investigating patients' medical results using advanced analysis (such as segmentation and predictive modelling, machine learning, visualisation, etc.).

Keywords:

• Health analytics
• big data analytics
• techniques
• capabilities
• bibliometrics
• sources of data

Notes

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.medicare.gov/physiciancompare/staticpages/resources/specialtydefinitions.html?AspxAutoDetectCookieSupport = 1