Methods and tools of formation of general indexes for automation of devices in rehabilitative medicine for post-stroke patients

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.239288

Keywords:

automation module, integral indicator, vector-indicator, lossless compression, recovery devices, public administration

Abstract

The current processes of recovery of post-infarction and post-stroke patients in the context of the establishment of the institution of family doctors and insurance medicine are considered. It was proposed to introduce modules for automation of recovery devices (MARD) to ensure procedures, quality of life and reduce labor costs during the period of long-term recovery. The forms of presentation of the model of the integral indicator are substantiated, which, in accordance with the requirements of the Ministry of Health, assesses the generalized indicator of the patient's statement (GIPS), the quality of medical services and increases the efficiency of data compression. A consistent application of two Euclidean norms is proposed, which leads indicators of dissimilar physical nature to a limited metric space. The relationship between the lower and upper bounds of the GIPS, the error, the width of the sliding window, and the values of the derivatives was established on the basis of the Taylor series expansion, geometric inequality and limited space. The model for evaluating the GIPS as a lower bound and the method for generating information about its properties are substantiated.

A three-level comparator is applied and an vector- indicator (VI) is introduced as an informational addition to the time series. Additional capabilities for intelligent analysis are demonstrated. The model of GIPS through VI is presented. The examples of VI values are used to demonstrate its applicability to the intelligent analysis of the recovery process. Openness, accessibility, transparency of GIPS and VI as tools of KIT is implemented by the princes of public administration (PA) by reducing it to quantitative control and comparison if there are quantitative and qualitative indicators in the list. VI, sliding windows, as PA and KIT tools in software (SW) for a diagnostic conclusion and correction of the course of procedures, are numerically investigated. It is demonstrated on examples of a numerical experiment with software how the combined application of the method for calculating the GIPS and VI effectively affects the compression ratio, increasing it to 60–75 %

Author Biographies

Alexandr Trunov, Petro Mohyla Black Sea National University

Doctor of Technical Science, Professor

Department of Automation and Computer-integrated Technologies

Volodymyr Beglytsia, Petro Mohyla Black Sea National University

Doctor of Science in Public Administration, PhD, Professor

Department of Local Self-Government and Regional Development

Gennady Gryshchenko, Petro Mohyla Black Sea National University

Candidate of Medical Sciences, Associate Professor, Director of Institute

Medical Institute

Viktor Ziuzin, Petro Mohyla Black Sea National University

Doctor of Medical Sciences, Professor, Head of Department

Department of Hygiene, Social Medicine, Public Health and Medical Informatics

Vitalii Koshovyi, Petro Mohyla Black Sea National University

Senior Lecturer

Department of Intelligent Information Systems

References

  1. Pro reabilitatsiyu u sferi okhorony zdorovia. Stattia 19. Nadannia reabilitatsiynoi dopomohy iz zastosuvanniam telereabilitatsiyi (2021). Verkhovna Rada Ukrainy, 8. Available at: https://zakon.rada.gov.ua/laws/show/1053-20#Text
  2. U 2020 rotsi likuvannia hostroho mozkovoho insultu ye priorytetom v prohrami medychnykh harantiy. Ministerstvo okhorony zdorovia Ukrainy (2019). Available at: https://www.kmu.gov.ua/news/u-2020-roci-likuvannya-gostrogo-mozkovogo-insultu-ye-prioritetom-v-programi-medichnih-garantij
  3. Unifikovanyi klinichnyi protokol medychnoi dopomohy. Ishemichnyi insult (ekstrena, pervynna, vtorynna (spetsializovana) medychna dopomoha, medychna reabilitatsiya). Zatverdzheno. Nakaz Ministerstva okhorony zdorovia 03.08.2012 No. 602. Available at: https://dec.gov.ua/wp-content/uploads/images/dodatki/2012_602/2012_602dod4ykpmd.pdf
  4. Pro zatverdzhennia indykatoriv yakosti medychnoi dopomohy. Nakaz MOZ Ukrainy vid 02.11.2011r. No. 743. Verkhovna Rada Ukrainy. Available at: https://zakon.rada.gov.ua/laws/show/z1328-11#Text
  5. Yakovleva, O. G. (2019). Main ways of formation and development of family medicine in Ukraine as the basis of reorganization of primary medical and sanitary aid for population. Nursing, 2, 16–21. doi: https://doi.org/10.11603/2411-1597.2019.2.10192
  6. Steel, A., Sibbritt, D., Schloss, J., Wardle, J., Leach, M., Diezel, H., Adams, J. (2017). An Overview of the Practitioner Research and Collaboration Initiative (PRACI): a practice-based research network for complementary medicine. BMC Complementary and Alternative Medicine, 17 (1). doi: https://doi.org/10.1186/s12906-017-1609-3
  7. Pro zatverdzhennia Kontseptsiyi upravlinnia yakistiu medychnoi dopomohy u haluzi okhorony zdorovia v Ukraini na period do 2020 roku. Nakaz MOZ Ukrainy vid 01.08.2011 No. 454. Verkhovna Rada Ukrainy. Available at: https://zakon.rada.gov.ua/rada/show/v0454282-11#Text
  8. Stallberg, B., Teixeira, P., Blom, C., Lisspers, K., Tsiligianni, I., Jordan, R. et. al. (2016). The prevalence of comorbidities in COPD patients and their impact on quality of life and COPD symptoms in primary care patients - An UNLOCK study from the IPCRG. 1.6 General Practice and Primary Care. doi: https://doi.org/10.1183/13993003.congress-2016.pa868
  9. Nahorna, A. M. (2003). Sotsialno-ekonomichni determinanty zdorovia naselennia Ukrainy (ohliad literatury i vlasnykh doslidzhen). Zhurnal AMN Ukrainy, 9 (2), 325–345.
  10. Hoida, N. H., Horachuk, V. V. (2011). Medyko-sotsiolohichna informatsiya yak instrument upravlinnia yakistiu medychnoi dopomohy. Tezy dopovidei konferentsiyi z mizhnarodnoiu uchastiu «Medychna ta biolohichna informatyka ta kibernetyka: vikhy rozvytku». Kyiv, 27.
  11. Melnykova, N. (2014). The features of decision making quality evaluation in medicine. Visnyk Natsionalnoho universytetu "Lvivska politekhnika", 805, 170–179. Available at: http://science.lpnu.ua/sisn/all-volumes-and-issues/volume-805-2014/osoblivosti-ocinyuvannya-yakosti-rezultativ-priynyattya
  12. Shchelkalin, V. (2015). A systematic approach to the synthesis of forecasting mathematical models for interrelated non-stationary time series. Eastern-European Journal of Enterprise Technologies, 2 (4 (74)), 21–35. doi: https://doi.org/10.15587/1729-4061.2015.40065
  13. Trunov, A. (2017). Recurrent Approximation in the Tasks of the Neural Network Synthesis for the Control of Process of Phototherapy. Computer Systems Healthcare and Medicine. Denmark, 213–248.
  14. The Ultimate Comparison of IOT Development Boards (2013). Open Electronics. Available at: https://www.open-electronics.org/the-ultimate-comparison-of-iot-development-boards/
  15. Tymoshchuk, P. V., Shatnyi, S. V. (2012). Systema monitorynhu ta keruvannia viddalenymy obiektamy rehuliuvannia. Naukovyi visnyk NLTU Ukrainy, 22, 313–318.
  16. Shatnyi, S., Shatna, A., Shablovska, A. (2019). Neural Network Hardware Implementation Using Micro- and Softprocessor Technologies for Biomedical Signal Processing. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 8 (8), 400–403. Available at: http://ijarcet.org/wp-content/uploads/IJARCET-VOL-8-ISSUE-8-400-403.pdf
  17. Trunov, A., Beglytsia, V. (2019). Synthesis of a trend’s integral estimate based on a totality of indicators for a time series data. Eastern-European Journal of Enterprise Technologies, 2 (4 (98)), 48–56. doi: https://doi.org/10.15587/1729-4061.2019.163922
  18. Mishchuk, O. (2019). Development of the method of forecasting the atmospheric air pollution parameters based on error correction by neural-like structures of the model of successive geometric transformations. Technology Audit and Production Reserves, 6 (2 (50)), 26–30. doi: https://doi.org/10.15587/2312-8372.2019.188743
  19. Mishchuk, O., Tkachenko, R., Pohrebennyk, V. (2019). The Accelerated Method of Filling Gaps in Data Using a Linear SGTM Neural-Like Structure. International Journal of Science and Engineering Investigations (IJSEI), 8 (91), 154–159. Available at: http://www.ijsei.com/papers/ijsei-89119-20.pdf
  20. Kovalchuk, A. M., Levytskyi, V. H. (2002). Rozrobka adaptyvnoho interfeisu korystuvacha prohramnoi systemy chyselnoho analizu matematychnykh zadach. Visnyk ZhITI, 20, 111–119.
  21. Bias, R.; Nielsen, J., Mack, R. (Eds.) (1994). The Pluralistic Usability Walkthrough: Coordinated Empathies. Usability Inspection Methods. John Wiley.
  22. Petrov, K. E., Kryuchkovskiy, V. V. (2009). Komparatornaya strukturno-parametricheskaya identifikatsiya modeley skalyarnogo mnogofaktornogo otsenivaniya. Kherson: Oldi-plyus, 294.
  23. Fisun, M., Smith, W., Trunov, A. (2017). The vector rotor as instrument of image segmentation for sensors of automated system of technological control. 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2017.8098828
  24. Boichenko, O. V. (2012). Osnovni pryntsypy proektuvannia yakisnoho prohramnoho zabezpechennia avtomatyzovanykh system upravlinnia. Measurement and computation technique in technological processes, 3, 88–91. Available at: https://journals.khnu.km.ua/index.php/MeasComp/article/view/1725/2191
  25. Horachuk, V. V. (2012). Upravlinnia yakistiu medychnoi dopomohy v zakladi okhorony zdorovia. Vinnytsia: PP Baliuk I.B., 18–23.
  26. Bellman, R. E., Kalaba, R. E. (1965). Quasilinearization and nonlinear boundary-value problems. American Elsevier Publishing Company.
  27. Trunov, A., Malcheniuk, A. (2018). Recurrent network as a tool for calibration in automated systems and interactive simulators. Eastern-European Journal of Enterprise Technologies, 2 (9 (92)), 54–60. doi: https://doi.org/10.15587/1729-4061.2018.126498

Downloads

Published

2021-08-31

How to Cite

Trunov, A., Beglytsia, V., Gryshchenko, G., Ziuzin, V., & Koshovyi, V. (2021). Methods and tools of formation of general indexes for automation of devices in rehabilitative medicine for post-stroke patients. Eastern-European Journal of Enterprise Technologies, 4(2(112), 35–46. https://doi.org/10.15587/1729-4061.2021.239288