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Issue 4 (194), article 4

DOI:https://doi.org/10.15407/kvt194.04.061

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

Kozak L.M., DSc (Biology), Senior Researcher,
Leading Researcher of the Medical Information Systems Department
e-mail: lmkozak52@gmail.com

Kovalenko A.S., DSc (Medicine), Professor,
Head of the Medical Information Systems Department
e-mail: askov49@gmail.com

Krivova O.A., Researcher of the Medical Information Systems Department
e-mail: ol.kryvova@gmail.com

Romanyuk O.A., Junior Researcher of Medical Information Systems Department
e-mail: ksnksn7@gmail.com

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

DIGITAL TRANSFORMATION IN MEDICINE: FROM FORMALIZED MEDICAL DOCUMENTS TO INFORMATION TECHNOLOGIES OF DIGITAL MEDICINE

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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REFERENCES

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: http://www.me.gov.ua/Documents/ (Last accessed: 06.07.18) (in Ukranian).

2. The Nine Elements of Digital Transformation. URL:  https://sloanreview.mit.edu/article/the-nine-elements-of-digital-transformation/?social_token=d65abc6db70ba459408562abb8de32bc &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).

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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).

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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).

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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.

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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

admin

Issue 182, article 7

DOI:https://doi.org/10.15407/kvt182.02.084

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)

СOMPLEX ESTIMATION OF REGIONAL DEMOGRAPHIC DEVELOPMENT

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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Received 01.06.2015