Big Data, Analyzing and Modelling: New Ways of Health Improvement and Regional Aspects

• Authors: Kopytko Vasyl , Shevchuk Lyubov , Yankovska Larysa , Semchuk Zhanna
• Languages of publication: EN

Abstracts

EN

The field of health improvement and life prolonging develops poorly, despite all the advances in medicine, chemistry and genetic engineering. Among the main problems is the difficulty of using new scientific achievements in other industries due to the rapid development of specialized knowledge, the problem of returning costs for the creation of really effective and the problem of aging population in developed countries. There are problems with data for this methods usage with privacy and security on different levels with regional peculiarities. Effective timing of work on health at the personal level can result as a result of increased time and productivity. But it's difficult for people to allocate their intellectual resources for that, so you have to connect artificial intelligence and machine learning. Big Data model with methods and analysis techniques on different levels for health improvement was suggested. The importance of the level of social networks and its regional aspects for the analysis of health improvement data was identified. Big data processing results implementation and levels of interaction with human with request for changes model was proposed. It consists from two levels of interaction with humans by level of quick reaction and discussion with smart personal assistance. Regional aspects from possible AI implementation in undeveloped countries were analyzed on example of personal level big data for health usage.

Keywords

EN

big data; analyzing; modelling; health improving; regional aspects

• Publisher: Altezoro s.r.o. (Slovak Republic) and Publishing Center "Dialog" (Ukraine)
• Journal: Path of Science
• Year: 2018
• Volume: 4
• Issue: 8
• Pages: 2017-2023

Dates

published

2018-08-31

Contributors

author

Kopytko Vasyl

• Lviv Branch of Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan

author

Shevchuk Lyubov

• Lviv University of Business and Law

author

Yankovska Larysa

• Lviv University of Business and Law

author

Semchuk Zhanna

• Lviv University of Business and Law

References

• Tae, K. (2018, April 18). Goldman Sachs asks in biotech research report: 'Is curing patients a sustainable business model?'. Retrieved from https://www.cnbc.com/2018/04/11/goldman-asks-is-curing-patients-a-sustainable-business-model.html
• Groves P., Kayyali B., Knott D., & Van Kuiken, S. (2013, January). The ‘big data’ revolution in healthcare. Accelerating value and innovation. Retrieved from https://www.ghdonline.org/uploads/Big_Data_Revolution_in_health_care_2013_McKinsey_Report.pdf
• Bhattacharyya, O., Khor, S., McGahan, A., Dunne, D., Daar, A. S., & Singer, P. A. (2010). Innovative health service delivery models in low and middle income countries - what can we learn from the private sector? Health Research Policy and Systems, 8(1). doi: 10.1186/1478-4505-8-24
• Pisani, E., & AbouZahr, C. (2010). Sharing health data: good intentions are not enough. Bulletin of the World Health Organization, 88(6), 462–466. doi: 10.2471/blt.09.074393
• Globa, L., Ishchenko, I., & Zakharchuk, A. (2017). Data Processing in E-Health System. Journal of Communication and Computer, 14(1). doi: 10.17265/1548-7709/2017.01.006
• Kurdecha, V., Ishchenko, I., Zakharchuk, A., & Kunieva, N. (2018). Fuzzy logic usage for the data processing in the Internet of Things networks. Retrieved from https://libeldoc.bsuir.by/handle/123456789/30355
• Shevchuk, A. V. (2016). Artificial Intelligence and Intellectualization: New Prospects for Economic Development. Scientific Review, 4(25), 27–36.
• Wang, X., White, L., & Chen, X. (2015). Big data research for the knowledge economy: past, present, and future. Industrial Management & Data Systems, 115(9). doi: 10.1108/imds-09-2015-0388
• Chen, A. (2017). Big Data and the Development of Regional Economy. Proceedings of the 2016 International Conference on Modern Management, Education Technology, and Social Science (MMETSS 2016). doi: 10.2991/mmetss-16.2017.32
• UN Global Pulse. (2012). Big data for development: challenges and opportunities. Retrieved from http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf
• London School of Economics and Political Science. (2010, December). Electronic health privacy and security in developing countries and humanitarian operations. Retrieved from http://personal.lse.ac.uk/martinak/ehealth.pdf
• World Economic Forum. (2011). Global health data charter. Retrieved from https://share.kaiserpermanente.org/media_assets/pdf/ceoletters/2011/downloads/WEFGlobalHealthDataCharter2011.pdf
• Statista. (2018). Forecast of Big Data market size, based on revenue, from 2011 to 2026 (in billion U.S. dollars). Retrieved July 1, 2018, from https://www.statista.com/statistics/254266/global-big-data-market-forecast
• Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data - SIGMOD ’93. doi: 10.1145/170035.170072
• Freeman, L. (2006). The Development of Social Network Analysis. Vancouver: Empirical Press.
• Freedman, D. A. (2012). Statistical models: Theory and practice. New York: Cambridge University Press.
• Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. doi: 10.1109/mis.2013.30
• Zhang, J., Chung, H. S.-H., & Lo, W.-L. (2007). Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms. IEEE Transactions on Evolutionary Computation, 11(3), 326–335. doi: 10.1109/tevc.2006.880727
• AIReligion. (2018, February 20). The development of artificial intelligence is hampered by programmers who hide their code. Retrieved from http://aireligion.org/?p=839

Identifiers

DOI

10.22178/pos.37-2