Big Data In healthcare: A to Z
Background & Aim: Today, with the advent of technology, due to the growing data in the field of health care, it is difficult to manage and analyze this type of data known as the Big Data. This analysis has many capabilities to improve the quality of care, reduce errors and reduce costs in care services.
Methods: This study is based on search of databases (PubMed, Google Scholar, Science
Direct, and Scopus). This investigation has done with the websites and the specialized books with standard key words. After a careful study, 50 sources were in the final article.
Results: Since the Big Data Analysis in the field of health has been growing and also
considered in recent years, this survey identified the necessity of these analyses, the definition of the Big Data, the benefits, resources, architecture, applications, analysis, platforms, Examples and challenges in the field of health care.
Conclusions: Familiarity with the big data concepts in the field of healthcare can help researchers in conducting applied research and thus improve the quality of health care services and reduce costs.
2. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health information science and systems. 2014;2(1):3.
3. Sagiroglu S, Sinanc D, editors. Big data: A review. Collaboration Technologies and Systems (CTS), 2013 International Conference on; 2013: IEEE.
4. Duggal PS, Paul S, editors. Big data analysis: challenges and solutions. International Conference on Cloud, Big Data and Trust; 2013.
5. subgroup TH. Big Data Technologies in Healthcare;Needs, opportunities and challenges. 2016.
6. Murdoch TB, Detsky AS. The inevitable application of big data to health care. Jama. 2013;309(13):1351-2. 2.
7. Chen M, Mao S, Liu Y. Big data: A survey. Mobile Networks and Applications. 2014;19(2):171-209 7. Mehta N, Pandit A. Concurrence of big data analytics and healthcare: A systematic review. International journal of medical informatics. 2018; 114:57-65.
8. Hermon R, Williams PA. Big data in healthcare: What is it used for? 2014.
9. Dexter PR, Miller DK, Clark DO, Weiner M, Harris LE, Livin L, et al. editors. Preparing for an aging population and improving chronic disease management. AMIA Annual Symposium Proceedings; 2010: American Medical Informatics Association.
10. Lewis SJ, Orland BI. The importance and impact of Evidence Based Medicine. Journal of Managed Care Pharmacy. 2004;10(5 Supp A):S3-S5.
11. Redekop WK, Mladsi D. The faces of personalized medicine: a framework for understanding its meaning and scope. Value in Health. 2013;16(6):S4-S9.
12. Harvey A, Brand A, Holgate ST, Kristiansen LV, Lehrach H, Palotie A, et al. The future of technologies for personalised medicine. New biotechnology. 2012;29(6):625-33.
13. Sox HC, Blatt MA, Higgins MC, Marton KI. Medical decision making: ACP Press; 2007.
14. Oshima Lee E, Emanuel EJ. Shared decision making to improve care and reduce costs. New England Journal of Medicine. 2013;368(1):6-8.
15. Sakkalis V, Zervakis M, Micheloyannis S, editors. Biopattern initiative: towards the development and integration of nextgeneration information fusion approaches. Engineering in Medicine and Biology Society, 2004 IEMBS'04 26th Annual International Conference of the IEEE; 2004: IEEE.
16. Rahman MM, Bhattacharya P. An integrated and interactive decision support system for automated melanoma recognition of dermoscopic images. Computerized Medical Imaging and Graphics. 2010;34(6):479-86.
7. Alemi F, Gustafson DH. Decision analysis for healthcare managers: Health Administration Press; 2007.
18. Rubinstein A. Modeling bounded rationality: MIT press; 1998.
19. Lee S, Lebowitz S. 20 cognitive biases that screw up your decisions. Business Insider. 2015.
20. Grandi U. Social choice and social networks. Trends in Computational Social Choice AI Access. 2017:169-84.
21. Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health affairs. 2011;30(3):464-71.
22. Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. International journal of medical informatics. 2008;77(5):291-304.
23. Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics. 2007;31(4-5):198211.
24. Scacchi W, editor Computer Games and Virtual Worlds: New Modalities of Rehabilitation and Therapy. Orange County.Stroke Rehab Network-Continuing Education Workshop; 2011.
25. Ventola CL. Mobile devices and apps for health care professionals: uses and benefits. Pharmacy and Therapeutics. 2014;39(5):356.
26. Terveen L, Hill W. Beyond recommender systems: Helping people help each other. HCI in the New Millennium. 2001;1(2001):487-509.
27. Melville P, Sindhwani V. Recommender systems. Encyclopedia of machine learning: Springer; 2011. p. 829-38.
28. Wiesner M, Pfeifer D. Health recommender systems: concepts, requirements, technical basics and challenges. International journal of environmental research and public health. 2014;11(3):2580-607.
29. Grajales III FJ, Sheps S, Ho K, NovakLauscher H, Eysenbach G. Social media: a review and tutorial of applications in medicine and health care. Journal of medical Internet research. 2014;16(2).
30. Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, et al. Making sense of big data in health research: towards an EU action plan. Genome medicine. 2016;8(1):71.
31. Archenaa J, Anita EM. A survey of big data analytics in healthcare and government. Procedia Computer Science. 2015; 50:40813.
32. Wang Y, Kung L, Byrd TA. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change. 2018;126:3-13.
33. Hermon R AHWP. Big data in healthcare: What is it used for? Australian eHealth Informatics and Security Conference. 2014.
34. Palanisamy V, Thirunavukarasu R. Implications of big data analytics in developing healthcare frameworks–A review. Journal of King Saud UniversityComputer and Information Sciences. 2017.
35. De Mauro A, Greco M, Grimaldi M, editors. What is big data? A consensual definition and a review of key research topics. AIP conference proceedings; 2015: AIP.
36. Sun J, Reddy CK, editors. Big data analytics for healthcare. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining; 2013: ACM.
37. Benke K, Benke G. Artificial Intelligence and Big Data in Public Health. International journal of environmental research and public health. 2018;15(12):2796.
38. Zhang X, Pérez-Stable EJ, Bourne PE, Peprah E, Duru OK, Breen N, et al. Big data science: opportunities and challenges to address minority health and health disparities in the 21st Century. Ethnicity & disease. 2017;27(2):95.
39. 40.Ramírez-Gallego S, Fernández A, García S, Chen M, Herrera F. Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce. Information Fusion. 2018; 42:51-61.
40. Ferranti A, Marcelloni F, Segatori A, Antonelli M, Ducange P. A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data. Information Sciences. 2017; 415:319-40.
41. Bifet A. Mining big data in real time. Informatica. 2013;37(1).
42. Groves P, Kayyali B, Knott D, Van Kuiken S. The ‘big data’revolution in healthcare. McKinsey Quarterly. 2013;2(3):1-22.
43. Kayyali B, Knott D, Van Kuiken S. The bigdata revolution in US health care: Accelerating value and innovation. Mc Kinsey & Company. 2013;2(8):1-13.
44. Jagadish H, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, et al. Big data and its technical challenges. Communications of the ACM. 2014;57(7):86-94.
45. Bossé É, Solaiman B. Information fusion and analytics for big data and IoT: Artech House; 2016. 46. Gunay O, Toreyin BU, Kose K, Cetin AE. Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video. IEEE Transactions on Image Processing. 2012;21(5):2853-65.
47. Li Y, Porter E, Santorelli A, Popović M, Coates M. Microwave breast cancer detection via cost-sensitive ensemble classifiers: Phantom and patient investigation. Biomedical Signal Processing and Control. 2017; 31:366-76.
48. Guo P, Banerjee K, Stanley R, Long R, Antani S, Thoma G, et al. Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification. Biomedical and Health Informatics. IEEE Journal of, PP (99). 2015.
49. Fan Y, Yin Y. Active and progressive exoskeleton rehabilitation using multisource information fusion from emg and forceposition epp. IEEE Transactions on Biomedical Engineering. 2013;60(12):331421.
50. Bussemaker HJ, Li H, Siggia ED. Regulatory element detection using correlation with expression. Nature genetics. 2001;27(2):167.
|Issue||Vol 5 No 3 (2019)|
|Big Data Healthcare datamining|
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