Design of information technology classification based on medical data

Authors

DOI:

https://doi.org/10.15587/2706-5448.2020.210671

Keywords:

medical and social data, medical statistics, risk groups, medical services, information and analytical support.

Abstract

Decision-making processes associated with assigning a person to a risk group for diseases are accompanied by the need to analyze large volumes of medical and social data. At the same time, a qualified doctor must operate both the patient's personal data and the relevant treatment protocols and instructions. Early prediction of the risks of disease occurrence allows medical workers to plan, develop a system of preventive measures, and the like. Therefore, the object of research is to support and provide information and analytical support for decision-making processes for early predicting the risks of diseases in individuals based on medical data. Such support is necessary to analyze the experience of a medical worker, which is recorded in the form of statistical data. One of the most problematic areas at the design and implementation stage of relevant information technology is the collection and analysis of statistical data on the problem under study.

The study was carried out in accordance with the systematic approach methodology. All stages of the design of information technology for forecasting based on medical data correspond to the stages of a systematic approach: systematization, formalization, goal orientation. The developed technology is based on the classification method based on Wald's sequential analysis.

The research resulted in:

- built a mathematical model of the problem of predicting the risks of disease occurrence as a classification problem;

- a functional diagram of an information and analytical system has been developed to solve the classification problem based on medical data. The analytical core of the information and analytical system is formed by algorithms for statistical data processing, as well as a classification method based on Wald's sequential analysis;

- experimental verification of the developed technology for the task of predicting the occurrence of dentoalveolar anomalies in children has been performed. Based on the available statistical data, a differential prognostic table was constructed. All calculations are performed. The examples demonstrate the effectiveness of the developed technology.

The developed information technology can be used by medical workers in the process of early forecasting of the risks of disease.

Author Biographies

Oksana Mulesa, State Higher Educational Institution «Uzhgorod National University» Narodna sq., 3, Uzhhorod, Ukraine, 88000

PhD, Associate Professor

Department of Cybernetics and Applied Mathematics

Vitaliy Snytyuk, Taras Shevchenko National University of Kyiv Volodymyrska str., 60, Kyiv, Ukraine, 01033

Doctor of Technical Sciences, Professor, Dean

Mykhailo Trombola, State Higher Educational Institution «Uzhgorod National University», Narodna sq., 3, Uzhhorod, Ukraine, 88000

Postgraduate Student

Department of Cybernetics and Applied Mathematics

Viktoriia Ivazkevych, State Higher Educational Institution «Uzhgorod National University», Narodna sq., 3, Uzhhorod, Ukraine, 88000

Lecturer

Department of Pediatric Dentistry

References

  1. Sun, L., Zou, L.-X., Han, Y.-C., Huang, H.-M., Tan, Z.-M., Gao, M. et. al. (2016). Forecast of the incidence, prevalence and burden of end-stage renal disease in Nanjing, China to the Year 2025. BMC Nephrology, 17 (1). doi: http://doi.org/10.1186/s12882-016-0269-8
  2. Li, J.-S., Zhang, Y.-F., Tian, Y. (2016). Medical big data analysis in hospital information system. Big data on real-world applications, 65. doi: http://doi.org/10.5772/63754
  3. Juang, W.-C., Huang, S.-J., Huang, F.-D., Cheng, P.-W., Wann, S.-R. (2017). Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan. BMJ Open, 7 (11), e018628. doi: http://doi.org/10.1136/bmjopen-2017-018628
  4. Steins, K., Matinrad, N., Granberg, T. (2019). Forecasting the Demand for Emergency Medical Services. Proceedings of the 52nd Hawaii International Conference on System Sciences. doi: http://doi.org/10.24251/hicss.2019.225
  5. Lopes, M. A., Almeida, Á. S., Almada-Lobo, B. (2016). Forecasting the medical workforce: a stochastic agent-based simulation approach. Health Care Management Science, 21 (1), 52–75. doi: http://doi.org/10.1007/s10729-016-9379-x
  6. Park, Y., Ho, J., Vishwanath, S. (2016). U.S. Patent Application No. 15/092,738.
  7. Amor, L. B., Lahyani, I., Jmaiel, M. (2016). Recursive and Rolling Windows for Medical Time Series Forecasting: A Comparative Study. 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES), 106–113. doi: http://doi.org/10.1109/cse-euc-dcabes.2016.169
  8. Kristianto, R. P., Utami, E. (2017). Optimization the parameter of forecasting algorithm by using the genetical algorithm toward the information systems of geography for predicting the patient of dengue fever in district of sragen, Indonesia. 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 45–50. doi: http://doi.org/10.1109/icitisee.2017.8285548
  9. Tymchenko, A. A. (2005). Systemnyi pidkhid do naukovoho doslidzhennia (orhanizatsiino-metodychni aspekty). Visnyk ChDTU, 1, 191–197.
  10. Mulesa, O. Yu., Snytiuk, V. Ye., Herzanych, S. O. (2020). A fuzzy classification method based on the sequential wald analysis. Automation of technological and business processes, 11 (4), 35–42. doi: http://doi.org/10.15673/atbp.v11i4.1597
  11. Herzanych, S. O., Mulesa, O. Yu. (2018). Alhorytm prohnozuvannia nevynoshuvannia vahitnosti v umovakh pryrodnoho yodnoho defitsytu. Zdorove zhenshchyni, 8 (134), 48–51.

Published

2020-08-31

How to Cite

Mulesa, O., Snytyuk, V., Trombola, M., & Ivazkevych, V. (2020). Design of information technology classification based on medical data. Technology Audit and Production Reserves, 4(2(54), 10–14. https://doi.org/10.15587/2706-5448.2020.210671

Issue

Section

Information Technologies: Original Research