VISUAL ANALYTICS — AN EFFECTIVE TECHNOLOGY FOR PROCESSING BIG DATA IN MEDICINE
DOI:
https://doi.org/10.11603/mie.1996-1960.2020.2.11173Keywords:
visual analytics, Big Data, medicineAbstract
Background. The analysis of the prospects for the use of visual analytics in clinical and experimental medicine, a healthcare, pharmacy and clinical research, primarily for processing Big Data, is presented. The analysis shows that visual analytics provides a more accessible and intuitive approach to the analysis of biomedical information, improves the efficiency of the use of collected and accumulated data, and reveals new and unknown knowledge by finding relationships, patterns, trends and anomalies in Big Data. Visual analytics provide data management, research and analysis. The developed methods for presenting data in the form of images, diagrams are aimed at the most complete use of registers of medical data, the use of accumulated information to predict the possibility of the development of diseases and their prevention, and in general should contribute to solving the problems of information overload. The above data indicate that the technology of visual analytics will contribute to a significant improvement in the quality of medical care for the population. The purpose of the study is to analyze the current state, problems and prospects of using Visual analytics technologies for Big Data analysis in medicine.
Materials and methods. Results. Visual analytics has great potential for Big Data processing, it allows for comprehensive data analysis to improve the quality of medical care and the analysis of a large number of complex heterogeneous data of various nature. Visual analytics allows you to increase the efficiency of using the collected and accumulated data, to discover new and unknown knowledge by finding relationships, patterns, trends and anomalies in Big Data. The use of VA will allow to solve the problem of information overload, which occurs when analyzing huge data arrays, to establish a relationship between a large number of variables.
Conclusions. VA methods allow clinicians, researchers, administrators, and patients to obtain effective, meaningful information from the vast and complex data resources that a modern healthcare system offers. At the same time, there are problems with the visual study of unstructured data, the lack of standardized methods for assessing, validating and measuring the effectiveness of IA tools, which requires further research.
References
Petrov, V. V., Mintser, O. P., Kryuchyn, A. A., Kryuchyna, E. A. (2019). Prospects and problems of the use of technology in medicine. Medical Informatics and Engineering, 3 (47), 20-30. doi: https://doi.org/10.11603/mie.1996-19602019.3.10429. [In Russian].
Petrov, V. V., Mintser, O. P., Kryuchyn, A. A., Kryuchyna, E. A. (2017). Problems of medical medical information. Medical Informatics and Engineering, 3, 52-62. doi: http://dx.doi.org/10.11603/mie.1996-1960.2017.3.8182. [ In Russian].
Dash, S., Shakyawar, S. K., Sharma, M., Kaushik, S. (2019). Big Data in healthcare: management, analysis and future prospects. Journal of Big Data , 6, 542.
Visual Analytics in Healthcare. (2014). Retrived from: http://visualanalyticshealthcare.org/.
Shishkin, Yu. E. (2017). Visual analysis of big data using cognitive patterns. Problems of Modern Science and Education, 2 (84), 221-225. [ In Russian].
Pasynkov, M. A. (2017). Integrated database integration system for monitoring physical parameters and positioning in water areas. Scientific journal, 2 (15), 29-31. [In Russian].
Ristevski, B., Chen, M. (2018). Big Data Analytics in Medicine and Healthcare. J Integr Bioinform, 15 (3), 2017-0030. doi: 10.1515/jib-2017-0030.
Stelmakh, S. (2018). VA-systems with AI and visual analytics will become the basis of a digital enterprise. Retrived from https://www.itweek.ru/about/authors/ detail.php? ID = 134156. [In Russian].
Caban, J. J., Gotz, D. (2015). Visual analytics in healthcare-opportunities and research challenges. J Am Med Inform Assoc., 22 (2), 260-262. doi: 10.1093/ jamia/ocv006.
Data visualization. (2015). Retrived from: http: //www. tadviser.ru/index.php.
Skatkov, A. V., Bryukhovetsky, A. A., Shishkin, Yu. E. (2016). Comparative analysis of methods for detecting changes in network traffic states. Automation and Instrument-Making: Problems, Solutions: International Materials. scientific and technical confer. Sevastopol, SevSU, 14-15. [In Russian].
Malyarova, M. V. (2016). Analytics and visualization of «big data»: why is «big data» a big problem? International Scientific Review, 3 (13), 66-68. [In Russian].
Kuznetsov, S. (2013). The Visual Future of Analytics. Review of Computer Journal (IEEE Computer Society, V. 46, No. 7, July 2013). Retrived from: http://citforum. ru/computer/2013-07/. [In Russian].
Troyanozhko, O. A., Kolesin, I. D. (2019). Visual analytics in digital biomedicine as an example of the quality of diagnosis of breast cancer. International Journal of Open Information Technologies, 7 (7), 2734. [In Russian].
Kobrinsky, B. A. (2012). The Importance of Visual Imagery for Medical Intelligent Systems. Artificial Intelligence and Decision Making, 3, 3-14. [In Russian].
Shishkin, Yu. E. (2017). Cloud services in decision support system. Scientific journal, 1 (14), 19-20. [In Russian].
Averbukh, V. L., Manakov, D. V. (2018). Analysis and visualization of «big data». Proceedings of the international scientific conference «Parallel Computing Technologies» (PaVT'2015). Yekaterinburg, March 31 - April 2, 2015, 332-340. [In Russian].
Big Data Visualization: Turning Big Data into Big Insights. The Rise of Visualization-based Data Discovery Tools. White Paper. Intel IT Center (2013). Retrived from: https://www.intel.com/ content/dam/www/public/us/ en/documents/white-papers/big-data-visualization-turning-big-data-into-big-insights.pdf.
Shneiderman, B. (2014). The big picture for big data: Visualization. Science. Science, 343 (6172), 730. doi: 10.1126/science.343.6172.730-a.
Simpao, A. F., Ahumada, L. M., Rehman, M. A. (2015). Big data and visual analytics in anaesthesia and health care. Br J Anaesth., 115(3), 350-6. doi: 10.1093/bja/ aeu552.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med., 6 (7), e1000097. doi: 10.1371/journal.pmed.1000097.
Yaeli, A. (2020). Visual Analytics Kit for Healthcare -overview. Currently real world data (RWD) is playing an increasing role in health care decision making. Retrived from: https://researcher.watson.ibm.com/researcher/ view_group.php.
Kashnitsky, U. S. (2014). Visual Analytics in the Triclusterization. Problem multidimensional data Proceedings of MIPT, 6 (3), 43-56. [In Russian].
Samoilenko, N. E., Kuvina, V. N., Kuvin, S. S. (2009). Comprehensive analysis of medical data. Bulletin of the Voronezh State Technical University, 5 (9), 114-118. [In Russian].
Chishtie, J. A., Babineau, J., Bielska, I. A. et al. (2019). Visual Analytic Tools and Techniques in Population Health and Health Services Research: Protocol for a Scoping Review. JMIR Res Protoc, 8 (10), e14019. http://doi.org/10.2196/14019.
Halford, G. S., Baker, R, McCredden, J. E., Bain, J. D. (2005). How many variables can humans process? Psychol Sci., 16 (1), 70-6. doi.org/10.1111%2Fj.0956-7976.2005.00782.x.
Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63 (2), 81-97.
Elyakov, A. D. (2010). Deficit and excess of information in modern society. Retrived from http://ecsocman.hse. ru/data/2011/03/11/ 1214896871/Elyakov_11.pdf. [In Russian].
Medical Imaging. Retrived from: http://zdrav.expert/ index.php/ file: G: / Visual Analytic in Healthcare / .html.
Benke, K., Benke, G. (2018). Artificial Intelligence and Big Data in Public Health. Int J Environ Res Public Health. Dec, 10; 15 (12), pii: E2796. doi: 10.3390/ ijerph15122796.
Qu, Z, Lau, C. W, Nguyen, Q. V., Zhou, Y., Catchpoole, D. R. (2019). Visual Analytics of Genomic and Cancer Data: A Systematic Review. Cancer Inform., 18:1176935119835546. doi: 10.1177/1176935119835546.
Luo W. (2016). Visual analytics of geo-social interaction patterns for epidemic control. Int J Health Geogr., 15, 28. doi: 10.1186/s12942-016-0059-3.
Qiu, H. J., Yuan, L. X., Huang, X. K., Zhou, Y. Q. et al. (2020). Using the big data ofinternet to understand coronavirus disease 2019's symptom characteristics: a big data study. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 55 (0), E004. doi: 10.3760/ cma.j.cn115330-20200225-00128.
Ting, D. S. W., Carin, L., Dzau, V., Wong, T. Y. (2020). Digital technology and COVID-19. Nat Med. Apr., 26 (4), 459-461. doi: 10.1038/s41591-020-0824-5.
Guo D. (2007). Visual analytics of spatial interaction patterns for pandemic decision support. Int J Geogr Inf Sci., 21 (8), 859-77. doi: 10.1080/13658810701349037.
Castronovo, D. A., Chui, K. K., Naumova, E. N. (2009). Dynamic maps: a visual-analytic methodology for exploring spatio-temporal disease patterns. Environ Health., 8, 61. doi: 10.1186/1476-069X-8-61.
Maciejewski, R., Rudolph, S., Hafen, R. et al. (2010). A visual analytics approach to understanding spatiotemporal hotspots. IEEE Trans Vis Comput Graph. 16 (2), 205-220. doi: 10.1109/TVCG.2009.100. Int J Health Geogr. 2016; 15: 28.
Wong Z. S. Y. (2-19). Artificial Intelligence for infectious disease Big Data Analytics. Infect Dis Health, 24:1, 4448. doi: 10.1016/j.idh.2018.10.002.
Wu, D. T. Y., Chen, A.T., Manning, J. D. et al. (2019). Evaluating visual analytics for health informatics applications: a systematic review from the American Medical Informatics Association Visual Analytics Working Group Task Force on Evaluation. J Am Med Inform Assoc., 26 (4), 314-323. doi: 10.1093/jamia/ ocy190.
Gu, D., Li, J., Li, X., Liang ,C. (2017). Visualizing the knowledge structure and evolution of big data research in healthcare informatics. Int J Med Inform., 98, 22-32. doi: 10.1016/j.ijmedinf.2016.11.006.
Ola, O., Sedig, K. (2014). The challenge of big data in public health: an opportunity for visual analytics. Online J Public Health Inform., 5 (3), 223. doi: 10.5210/ojphi. v5i3.4933. eCollection 2014.
Mehta, N., Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform., 114, 57-65. doi: 10.1016/j. ijmedinf.2018.03.013.
Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., Noor-E-Alam, M. (2018). A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining. Healthcare (Basel), 6 (2), 54. doi:10.3390/healthcare6020054.
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