A SYSTEMATIC APPROACH TO SOLVING APPLIED MEDICAL DIAGNOSTIC PROBLEMS
Abstract and keywords
Abstract (English):
The use of modern technologies in various fields of activity is a prerequisite for the development of diagnostic systems, and in particular, self-diagnosis systems for human health. The development of medicine, computer and computing technology, information technology contributes to this. The rhythm of life of a modern person and the availability of specialized diagnostic systems allows a person to do without going to medical institutions. This is also due to the current situation during the pandemic. Diagnostic systems give an impetus to the user to see a doctor if he ultimately understands the need for this. Then the user of the system with an established primary diagnosis becomes a patient of the clinic. Research in the medical field and data processing are carried out with the involvement of high-tech technologies, powerful computers, high-performance methods and algorithms. Diagnostic systems are developed as web or cross-platform applications. A mandatory element of the applications is a questionnaire, the quality of which determines the correctness of the diagnosis. The conducted analysis of existing freely available applications of various profiles showed that at the moment there is no application for diagnosing eye diseases in a non-invasive way. The relevance of the application development is explained by the modern flow of life: the use of laptops, desktop computers and all sorts of gadgets that automatically assume the presence of screens with different resolutions. If the operating mode is not observed and the use of computing equipment is aggravated, optic nerve strain is observed, which can contribute to the development of serious eye diseases. The paper presents the conclusions of medical data processing confirming the negative impact of unfavorable factors on the human visual organs. The causes of eye diseases and symptoms in various diseases are systematized. The analysis of the collected data showed their diverse nature. A decision support model based on the fuzzy logic apparatus for studying the state of the human eyes has been developed. The possibility of diagnosing the state of human organs by the iris of the eye is shown

Keywords:
Diagnosis, narrow-focused system, linguistic variable, inference rules, fuzzy composition, knowledge base model, fuzzy rules
References

1. Zhukova A. Virtual'nye klony: kak cifrovye dvoyniki pacientov pomogayut vracham – Rezhim dostupa: https://scientificrussia.ru/articles/virtualnye-klony-kak-cifrovye-dvojniki-pacientov-pomogaut-vracam Zagl. s ekrana (data obrascheniya 21.02.2024)

2. Eynullaev, T. A. Cifrovye dvoyniki i ih rol' v ekonomicheskom modelirovanii / T. A. Eynullaev, L. A. Korobova // Modelirovanie energoinformacionnyh processov : Sbornik statey XII nacional'naya nauchno-prakticheskaya konferenciya s mezhdunarodnym uchastiem, Voronezh: Voronezhskiy gosudarstvennyy universitet inzhenernyh tehnologiy, 2024. – S. 319-323. – EDN KDUHFH.

3. Abgaryan, K.K. Mnogomasshtabnoe modelirovanie v zadachah strukturnogo materialovedeniya: monografiya / K. K. Abgaryan. – Moskva: MAKS Press, 2017. – 284 s.

4. Haug, Ch. J. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023 / Ch. J. Haug, J. M. Drazen // New England Journal of Medicine. – 2023. – Vol. 388, No. 13. – P. 1201-1208. – DOIhttps://doi.org/10.1056/nejmra2302038. – EDN TXQTNL.

5. Korobova, L. A. Sovremennye vozmozhnosti mnogomasshtabnogo modelirovaniya v reshenii prikladnyh prognosticheskih medicinskih zadach / L. A. Korobova, A. V. Kalach // Aktual'nye problemy deyatel'nosti podrazdeleniy ugolovno-ispolnitel'noy sistemy : sbornik materialov Vserossiyskoy nauchno-prakticheskoy konferencii, Voronezh: Izdatel'sko-poligraficheskiy centr "Nauchnaya kniga", FKOU VO Voronezhskiy institut FSIN Rossii., 2023. – S. 406-409. – EDN PEZTYD.

6. Program Development for Choosing a Surgical Treatment Option and Mathematical Prediction of Findings in Patients with Postoperative Median Abdominal Hernias / D. V. Arapov, E. F. Cherednikov, S. A. Skorobogatov [et al.] // International Journal of Biomedicine. – 2022. – Vol. 12, No. 2. – P. 303-307. – DOIhttps://doi.org/10.21103/Article12(2)_OA19. – EDN HYJZRL.

7. Diagnostika zabolevaniy metodami teorii veroyatnostey / M. L. Zhmudyak [i dr.]. – Barnaul: Izd-vo AltGTU, 2006. – 170 s. – ISBN 5-7568-0524-9. – EDN QLNMYD.

8. The Study of Fuzzy Quantifiers in Multi-criteria Decision-Making / M. Matveev, N. Alejnikova, V. Safonov, L. Korobova // Communications in Computer and Information Science. – 2022. – Vol. 1539. – P. 167-179. – DOIhttps://doi.org/10.1007/978-3-030-95494-9_14. – EDN QSPNIN.

9. Gladkih, T. V. Razrabotka sistemy podderzhki prinyatiya resheniy na osnove issledovaniy statisticheskih dannyh dlya diagnostiki zabolevaniy / T. V. Gladkih, L. A. Korobova // Vestnik Ryazanskogo gosudarstvennogo radiotehnicheskogo universiteta. – 2018. – № 66-1. – S. 130-138. – DOIhttps://doi.org/10.21667/1995-4565-2018-66-4-1-130-138. – EDN PKITTD.

10. Abramov, G. V. Proektirovanie informacionnyh sistem: Uchebnoe posobie / G. V. Abramov, I. E. Medvedkova, L. A. Korobova. – Voronezh : Voronezhskiy gosudarstvennyy universitet inzhenernyh tehnologiy, 2012. – 172 s. – EDNZVCUUV.

11. Iomdina E.N., Poloz M.V. Biomehanicheskaya model' glaza cheloveka kak osnova dlya izucheniya ego akkomodacionnoy sposobnosti. Rossiyskiy zhurnal biomehaniki. 2010. T. 14. № 3(49). C.7–18.

12. Poloz M.V. Biomehanicheskaya model' glaznogo yabloka cheloveka: dis. kand. fiz.-mat. nauk. Moskva, 2011.

13. Rajpurkar, P. The Current and Future State of AI Interpretation of Medical Images / P. Rajpurkar, M. P. Lungren // New England Journal of Medicine. – 2023. – Vol. 388, No. 21. – P. 1981-1990. – DOIhttps://doi.org/10.1056/nejmra2301725. – EDN QVJDIZ.

14. Korobova, L. A. Razrabotka modeli prinyatiya resheniya dlya postanovki diagnoza zabolevaniy na osnove nechetkoy logiki / L. A. Korobova, T. V. Gladkih // Vestnik Voronezhskogo gosudarstvennogo universiteta inzhenernyh tehnologiy. – 2018. – T. 80, № 4(78). – S. 80-89. – DOIhttps://doi.org/10.20914/2310-1202-2018-4-80-89. – EDN RJTMUW.

15. Krasnopeeva I. Yu., Sizyh T. P. Iridodiagnostika kak skrining-metod v klinike vnutrennih bolezney // Sibirskiy medicinskiy zhurnal. - 1998. - №1. – T.12. – S. 34-44.

16. Antipov, E.V. Obosnovanie vozmozhnostey metoda iridodiagnostiki dlya kompleksnoy ocenki funkcional'nogo sostoyaniya vnutrennih organov / E.V. Antipov, O.N., Kiseleva, Yu.V. Pervova, T.V. Starikova. – g. Samara // Vestnik medicinskogo instituta «REAVIZ» - 2019 - № 4 - S. 169-175

17. Blender 4.1.0 Blender Foundation Tekst: elektronnyy – URL: https://blender.ru.uptodown.com/windows Zagl. s ekrana (data obrascheniya: 30.03.2024)

18. Kanski D.Dzh. Klinicheskaya oftal'mologiya: sistematizirovannyy podhod. Pod red. V.P. Ericheva. M.: Logosfera, 2006. C. 517–522.

19. Shamshinova A.M., Volkov V.V. V: Funkcional'nye metody issledovaniya v oftal'mologii. M.: Medicina, 1999. C. 361–365.

20. Gladkih, T. V. Primenenie nechetkih mnozhestv dlya diagnostiki glaznyh zabolevaniy / T. V. Gladkih, L. A. Korobova, I. S. Tolstova // Innovacii v sozdanii materialov i metodov dlya sovremennoy mediciny: materialy regional'noy konferencii. – Voronezh: Voronezhskiy gosudarstvennyy universitet inzhenernyh tehnologiy, 2020. – S. 161-167. – EDN KDANAV.

21. Korobova, L. A. The use of logical-linguistic apparatus to describe the functioning of medical systems / L. A. Korobova, I. A. Matytsina // JOP Conference Series: Metrological Support of Innovative Technologies, Krasnoyarsk, 04 marta 2020 goda / Krasnoyarsk Science and Technology City Hall of the Russian Union of Scientific and Engineering Associations. Vol. 1515. – Krasnoyarsk, Russia: Institute of Physics and IOP Publishing Limited, 2020. – P. 52020. – DOIhttps://doi.org/10.1088/1742-6596/1515/5/052020. – EDN LJMCUE.

22. Uhobotov, V. I. Izbrannye glavy teorii nechetkih mnozhestv: ucheb. posobie / V. I. Uhobotov. – Ch.: Izd-vo Chelyab. gos. un-ta, 2011. – 245 s. Makeeva, A.V. Osnovy nechetkoy logiki. Uchebnoe posobie dlya vuzov / A.V. Makeeva. – N. Novgorod: VGIPU, 2009. – 59 s.

23. Kaid, V.A.A. Metody postroeniya funkciy prinadlezhnosti nechetkih mnozhestv / V.A.A. Kaid // Izvestiya YuFU. Tehnicheskie nauki.– 2013. – №2 (139). – S. 144–153.

24. Korobova, L. A. Logiko-lingvisticheskoe modelirovanie processa diagnostiki / L. A. Korobova, I. A. Matycina // Modelirovanie energoinformacionnyh processov: Sbornik materialov VII nacional'noy nauchno-prakticheskoy konferencii s mezhdunarodnym uchastiem. – Voronezh: Voronezhskiy gosudarstvennyy universitet inzhenernyh tehnologiy, 2019. – S. 405-408. – EDN QRCKJD.

25. The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis / Ch. Chang, W. Shi, Y. Wang [et al.] // Computers in Biology and Medicine. – 2024. – Vol. 172. – P. 108258. – DOIhttps://doi.org/10.1016/j.compbiomed.2024.108258. – EDN OXUIFZ.

Login or Create
* Forgot password?