A review of the literature on big data analytics in healthcare
Panagiota Galetsi and
Korina Katsaliaki
Journal of the Operational Research Society, 2020, vol. 71, issue 10, 1511-1529
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.).
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2019.1630328 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:71:y:2020:i:10:p:1511-1529
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20
DOI: 10.1080/01605682.2019.1630328
Access Statistics for this article
Journal of the Operational Research Society is currently edited by Tom Archibald
More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().