Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study

被引:81
作者
Mehta, Nishita [1 ]
Pandit, Anil [2 ]
Shukla, Sharvari [3 ]
机构
[1] Symbiosis Int Deemed Univ, Pune, Maharashtra, India
[2] Chellaram Diabet Inst, Pune, Maharashtra, India
[3] Symbiosis Int Deemed Univ, Symbiosis Stat Inst, Pune, Maharashtra, India
关键词
Healthcare; Big data analytics; Machine learning; Artificial intelligence; Systematic map;
D O I
10.1016/j.jbi.2019.103311
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The domain of healthcare has always been flooded with a huge amount of complex data, coming in at a very fast-pace. A vast amount of data is generated in different sectors of healthcare industry: data from hospitals and healthcare providers, medical insurance, medical equipment, life sciences and medical research. With the advancement in technology, there is a huge potential for utilization of this data for transforming healthcare. The application of analytics, machine learning and artificial intelligence over big data enables identification of patterns and correlations and hence provides actionable insights for improving the delivery of healthcare. There have been many contributions to the literature in this topic, but we lack a comprehensive view of the current state of research and application. This paper focuses on assessing the available literature in order to provide the researchers with evidence that enable fostering further development in this area. A systematic mapping study was conducted to identify and analyze research on big data analytics and artificial intelligence in healthcare, in which 2421 articles between 2013 and February 2019 were evaluated. The results of this study will help understand the needs in application of these technologies in healthcare by identifying the areas that require additional research. It will hence provide the researchers and industry experts with a base for future work.
引用
收藏
页数:14
相关论文
共 26 条
[1]   Big Data: transforming drug development and health policy decision making [J].
Alemayehu D. ;
Berger M.L. .
Health Services and Outcomes Research Methodology, 2016, 16 (3) :92-102
[2]  
[Anonymous], 2007, ENGINEERING
[3]   Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients [J].
Bates, David W. ;
Saria, Suchi ;
Ohno-Machado, Lucila ;
Shah, Anand ;
Escobar, Gabriel .
HEALTH AFFAIRS, 2014, 33 (07) :1123-1131
[4]  
Brownlee J., 2013, A Tour of Machine Learning Algorithms
[5]   Leveraging Big Data to Transform Target Selection and Drug Discovery [J].
Chen, B. ;
Butte, A. J. .
CLINICAL PHARMACOLOGY & THERAPEUTICS, 2016, 99 (03) :285-297
[6]   Big data in biomedicine [J].
Costa, Fabricio F. .
DRUG DISCOVERY TODAY, 2014, 19 (04) :433-440
[7]   Empirical studies of agile software development:: A systematic review [J].
Dyba, Tore ;
Dingsoyr, Torgeir .
INFORMATION AND SOFTWARE TECHNOLOGY, 2008, 50 (9-10) :833-859
[8]  
Eeles P., 2006, What is a Software Architecture
[9]  
Gutierrez D., 2016, INSIDEBIGDATA GUIDE
[10]   Big Data and Adverse Drug Reaction Detection [J].
Harpaz, R. ;
DuMochel, W. ;
Shah, N. H. .
CLINICAL PHARMACOLOGY & THERAPEUTICS, 2016, 99 (03) :268-270