Issue 4 (194), article 4


Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Kozak L.M., DSc (Biology), Senior Researcher,
Leading Researcher of the Medical Information Systems Department

Kovalenko A.S., DSc (Medicine), Professor,
Head of the Medical Information Systems Department

Krivova O.A., Researcher of the Medical Information Systems Department

Romanyuk O.A., Junior Researcher of Medical Information Systems Department

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Glushkov ave., 40, Kiev, 03187, Ukraine


Introduction. According to the Concept of Ukraine`s Digital Economy and Society Development in 2018-2020, the key components of “digitalization” are the development of digital infrastructure — broadband Internet throughout Ukraine, and the promotion of digital transformations in various sectors of the economy and society, including medicine.

The purpose of the paper is to analyze the stages of digital transformation in medicine and the results of authors and their colleagues of the MIS department for the development of information technologies of digital medicine.

Results. A generated model of digital transformation in medicine is presented and several main stages of this transformation are highlighted: І — digital transformation of primary medical information; ІІ — development of support systems for the diagnostic and treatment process; ІІІ — development of technologies and systems for supporting the physicians` activities with digital information; IV — mobile medicine; V — the digital medicine globalization. The method of determining the markers of the functional state of the cardiovascular system based on mathematical models of forecasting and classification with the use of Data Mining is proposed. The method allows detecting and determining the prognostic values of ECG parameters of the CVS functional state for different groups of patients. The developed IT for supporting the processes of receiving, transmitting and storing digital medical images is aimed at ensuring the effective operation of a physician with digital information from various sources: functional diagnostic complexes, digital medical data storage and images using Picture Archiving and Communication Systems (PACS) and cloud technologies . The proposed telemedicine systems theory including the formulated principles of organizing these systems, criteria and methods for analyzing digital medical data has been implemented for elaborating and functioning the Telemedicine Centre. It enables to cover the population in more than 20 Ukraine`s regions with qualified medical assistance.

Conclusions. The digital transformation in medicine like any new process takes place with a gradual complication of tasks, methods and means of their implementation: from formalization of primary medical information to improvement of methods of its analysis, transfer and storage to improve the quality of medical care for patients at any point of the world.

Keywords: digital transformation in medicine, formalized medical records, Data Mining, IT for assessing human state and physiological systems` state, telemedicine, m-medicine.

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1. About the conceptualization of the concept of the development of the digital economy of Ukraine and 2018–2020 on the basis of the plan set for the project: Disposition of the Government of Ukraine. URL: (Last accessed: 06.07.18) (in Ukranian).

2. The Nine Elements of Digital Transformation. URL: &utm_source= facebook&utm_medium=social&mmmmmt (Last accessed: 27.06.18)

3. Medical information system. Kyiv: Nauk. Dumka, 1975. 508 p. (in Russian).

4. A. p. № 2002032456 Ukraine MKI. Method for the diagnosis of local changes in the myocardium state. V.A. Petrukhin, V.N. Mamaev, A.S. Kovalenko, T.V. Petrukhina, V.A. Shumakov. Announced 15.01.2003; publ. 03.28.2003. (in Russian).

5. Provotar A.I., Vasilik P.V. Model waves and interaction: Theoretical and applied as-pects. Kyiv: Nauk. Dumka, 2014. 296 p (in Russian).

6. Vasilik P.V., Lychak M.M. Possible interactions in the Solar System and the synchronism of cyclical variations in solar activity with climatic changes on Earth. Geophysical journal. 2012. V. 34, No. 1. P. 138–158. (in Russian).

7. Vasilik P.V., Vasilega A.G., Chekaylo M.A. Influence of disturbances of space environmental factors on the accident rate of the objects of ground infrastructure and the accident rate on transport. Kibernetika i vyčislitel`naâ tehnika, 2011, Issue 166. P. 74–84 (in Russian).

8. Kozak L.M., Lukashenko M.V. The use of information models and integral assessments of the functional state of students for the formation of programs of psychological support. Integrative anthropology. 2008. №2 (12). P. 51–57 (in Russian).

9. Kozak L.M., Lukashenko M.V. Monitoring and correction students ’functional state. Kibernetika i vyčislitel`naâ tehnika. 2014. Issue 176. P. 74–84.

10. M.L. Kochina, L.M. Kozak, A.S. Yevtushenko Analysis of changes in the factor structures of indicators of the functional state of a person with different types of visual load. Bulletin of problems of biology and medicine. 2013, Iss. 1, Vol. 1 (98), pp. 41–45 (in Russian).

11. Evtushenko A.S., Kozak L.M., Kochina M.L. Evaluation of the relationship structure be-tween the functional indicators of operators in visual work using factor models. Kiber-netika i vyčislitel’naâ tehnika. 2016. Vol. 185. P. 60–76 (in Russian).

12. Rogozinskaya N.S., Kozak L.M. Information support of technology for automated moni-toring of the health of the population. Kibernetika i Sistemnyj Analiz. 2013. № 6. P. 162-173 (in Russian).

13. Rogozinskaya N.S., Kozak L.M. Complex indicators for the analysis of causal mortality of the population. Clinical informatics and telemedicine. 2013. Vol. 9, Iss. 10. P. 108–116 (in Russian).

14. Rogozinskaya N.S., Kozak L.M. Information technology research of the state of health of the population of the region. Upravlâûŝie sistemy i mašiny. 2013. № 6. P. 59–67 (in Russian).

15. Krivova O.A., Kozak L.M. Comprehensive assessment of regional demographic devel-opment. Kibernetika i vyčislitel`naâ tehnika. Issue 182. 2015. P. 70–84 (in Russian).

16. Krivova, OA, Tchaikovsky, I.A., Kalnish, VV, Kozak, L.M. Vidbіr informative shows the variability of the rhythm of the heart – the markers of the reaction to his stimulation. Medical informatics and engineering. 2016. No. 2. pp. 37–44 (in Russian).

17. Tchaikovsky I.A. The concept of multilateral analysis of the electrocardiogram using portable electrocardiographs as part of a preventive medical examination. Preventive medicine. 2014. No. 17 (2). P. 42–48 (in Russian).

18. Sposib Universalnoyi Balnoyi Otcinky EKG: Budnik M.M., Staryska G.A., Tchaikovsky І.A. Pat. 104827 Ukraine, IPC: A61B 5/0402, A61B 5/0205; declare 07.13.2015; publ. 02/25/2016, Bul. No. 4.

19. EN ISO 12052: 2011. Health informatics. Digital work, including workflow and data management URL: (Last accessed: 23.01.18)

20. Romanyuk O. A., Kovalenko A.S., Kozak L.M. Information support interoperability of instrumental studies and long-term storage of digital medical imaging in health care sys-tem. Kibernetika i vyčislitel`naâ tehnika. 2016. Iss. 184. P. 56–71 (in Russian).

21. Kovalenko A.S., Kozak L.M., Romanyuk O.A. Information technology of digital medi-cine. Kibernetika i vyčislitel`naâ tehnika. 2017. №1(187). P.67–79. (in Russian).

22. Kovalenko A.S., Kozak L.M., Ostashko V.G. Telemedicine — the development of a sin-gle medical information space. Upravlâûŝie sistemy i mašiny. 2005. № 3. P. 86–92 (in Russian).

23. Gritsenko V.I., Kozak L.M., Kovalenko A.S., Pezenzali A.A., Rogozinskaya N.S., Ostashko V.G. Medical information systems as elements of a unified medical informa-tion space. Kibernetika i vyčislitel`naâ tehnika, 2013, Iss. 174. P 30-46 (in Russian).

24. Kovalenko O.S., Kozak L.M., Romaniuk O.O., Maresova T.A., Nenasheva L.V., Fyniak G.I. Mobile applications in the structure of modern medical information systems. Upravlâûŝie sistemy i mašiny, 2018, №4. P. 57-69.


Received 29.08.2018

Issue 182, article 7


Kibern. vyčisl. teh., 2015, Issue 182, pp.

Krivova O.A., Kozak L.M.

International Research and Training Center for Information Technologies and
Systems of the National Academy of Sciences of Ukraine and Ministry of Education
and Science of Ukraine (Kiev)


Introduction. Several studies are being conducted in the world to measure developmental disparities between countries, regions and territorial units. Composite indicators (or indexes) are used whenever a lot of variables are needed for evaluating developmental disparities between territories. Demographic variables are considered as important indicators of socio-economic development of regions. We show how cluster analysis can be combined with elements of multicriteria decision analysis (MCDA) to construct composite index regional demographic development of Ukraine.
The purpose of this article is the development of regional socioeconomic systems analysis methodology and construction of composite indicators of regional demographic development.
Results. We have used 5 territorial social-demographic indicators: 1) total fertility rate; 2) death rate of children under age of five; 3) life expectation at birth; 4) survivorship probabilities for men from 20 to 65 years; 5) survivorship probabilities for women from 20 to 65 years. The following strategy can be pursued in order to construct composite index . First, a cluster analysis (algorithms Ward and K-means) for defining clusters of regions based on the value of the individual indicators is used. The result of the cluster analysis is typological clusters of the selected regions. Second, such as each cluster can be characterized with a centroid, these centroids must be ordered from best to worst. Weights of composite index are calculated as coefficients of the best linear regression model of preference function.
Conclusion. The composite index of regional demographic development allows to assess the degree of variance in regional demographic development and ranking of regions.
Keywords: clustering, a composite indicator, the index of regional demographic development, ordered classification.

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  1. Methods of measuring human development regions of Ukraine; Resolution adopted by the College of the State Statistics Committee of Ukraine and Presidium of NAS of Ukraine 14.03.2001 р. № 76 Available at: (in Ukrainian).
  2. Regional statistics. Statistical Yearbook Regional Human Development Available at: (in Ukrainian).
  3. Libanova E.M. Human development in Ukraine: transformation of the standard of living and regional disproportion — Kyiv: IDSS NANU — 2012. — 436 p. (in Ukrainian).
  4. Porter M. E. Location, competition, and economic development: Local clusters in a global economy // Economic Development Quarterly — 2000. — vol. 14. — №1— pp. 15–34.
  5. Izard W. Methods of Regional Analysis: An Introduction to Regional Science. — M.: Progress — 1960. — 660 p. (in Russian).
  6. Granberg A.G. Fundamentals of regional economy. 4th ed. — M.: GU HSE — 2004. — 495 p. (in Russian).
  7. Libanova E.M. Human development of the regions of Ukraine: assessment methodology and current state — Kyiv. — 2002. — 110 p. (in Ukrainian).
  8. Gerasymchyk Z.V. Regional policy of sustainable development: theory, methodology, practice — Lutsk: LNTU — 2011. — 260 p. (in Ukrainian).
  9. Ayvazian S. A. Integrated indicators of the quality of life of the population: their construction and use in socio-economic governance interregional comparisons — M.: TsEMI RAN — 2000. — 117 p. (in Russian).
  10. Hagerty. M.R., Land K.C. Constructing Summary Indices of Quality of Life A Model for the Effect of Heterogeneous Importance Weights// Sociological Methods Research — 2007. — vol. 35. — № 4. — pp.455–496.
  11. Shishkіn V.S. Conceptual and methodological approaches to regional disparities in human development//Statistics of Ukraine — 2001. — № 1. — pp. 30–38. (in Ukrainian).
  12. Makarova, O.V., Hladun O.M. Regional Human Development Index: Causes and areas of improvement methods of calculation // Statistics of Ukraine — 2012. — № 1. — pp. 10–15. (in Ukrainian).
  13. Grygoruk P.M., Tkachenko I.S. Methods of Integral Index Construction // Business Inform — 2012 — №4. — pp. 34–38. (in Ukrainian).
  14. Makotsoba M.B. Integrated estimation, ranking and clustering regions of Ukraine in terms of progress in human development // Economy and management — 2012. — № 4. — pp. 144–151. (in Ukrainian).
  15. Ohlikh V.V., Yefanova T.I. Regional space: methodical approach to the assessment of intraregional differentiation// Regional economy — 2014. — № 4. — pp. 40–47. (in Ukrainian).
  16. Composite Indicators Research Group Available at:
  17. Handbook on constructing composite indicators. Methodology and user guide OECD/JRC. 2008 Available at:
  18. Freudenberg M. Composite indicators of country performance: a critical assessment — Paris: OECD, STI WORKING PAPER — 2003. — № 16. — 34 p.
  19. The typology of Russian regions/B. Boots, S. Drobyshevskyi, O. Kochetkova — М.: Gaidar institute for Economic Policy, CEPRA — 2002. — 159 p. (in Russian).
  20. Ignatieva A., Maryev O. Methodical attitude to analyses of regional development stability on the basis of Kohonen’s self-organizing maps//Economy of region, Institute of Economic — Yekaterinburg — 2008. — № 2. — pp. 116–129. (in Russian).
  21. Saisana M., Saltelli A., Tarantola S. Uncertainty and sensitivity analysis techniques as tools for the analysis and validation of composite indicators//Journal of the Royal Statistical Society — Blackwell Publishing — 2005. — vol. 168. — № 2. — pp. 307–323.
  22. Ishizaka A, Nemery P. Multi-criteria decision analysis: methods and software. 1st ed. — Chichester: Wiley — 2013. — 310 p.
  23. Saaty T. Decision Making. The Analytic Hierarchy Process — М.: Radio Sviaz. — 1993. — 278 p. (in Russian).
  24. Mironova N. O. Integration modifications of the analytical hierarchy process for group decision making support systems//Radio Electronics, Computer Science, Control — Zaporizhzhya, National Technical University — 2011. — № 2. — pp. 47–54. (in Russian).
  25. Smet Y.D., Nemery P., Selvara R. An exact algorithm for the multicriteria ordered clustering problem//Omega. — 2012. — vol. 40. — № 6. — pp. 861–896.
  26. Mirkin B. G. Methods of cluster analysis for support decision-making. Review. Preprint WP7/2011/ 03 Available at: http:/ /www.hse. ru/data/2013/ 03/23/1303511006/WP7_2011_03f.pdf. (in Russian).
  27. Fernandez. E., Navarro J., Bernal S. Handling multicriteria preferences in cluster analysis//European Journal of Operational Research. — 2010, — vol. 202, — №3, — pp. 819–827.
  28. Methods of measuring human development regions of Ukraine — Kyiv: IDSS NANU, State Statistics Service of Ukraine. — 2012. — 41 p. Available at:
  29. Krivova O.A., Kurilo I.A. The regional typology the natural growth of the population of Ukraine: Cluster approach//Cybernetics and Computer Engineering — 2011. — №. 164. — pp. 89–102. (in Russian).

Received 01.06.2015