DOI
10.34229/KCA2522-9664.24.6.15
UDC 519.8.812.007
1 V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
aralova@ukr.net
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2 V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
g.ckikrii@gmail.com
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INTELLIGENT SYSTEM OF DIAGNOSTIC HUMAN ADAPTATION
TO EXTREME DISTURBANCE
Abstract. An intelligent system for diagnosing the human body’s adaptation to extreme disturbances of the external and internal environment is proposed. The components of the system are mathematical models of transport and mass exchange of respiratory gases, self-organization of the respiratory and circulatory systems, heat exchange and heat transfer systems, and the immune response system, their interaction and mutual influence.
Keywords: intelligent diagnostic system, mathematical model of the functional respiratory system, interaction and mutual influence of the functional systems of the body, adaptation of the body to extreme disturbances.
full text
REFERENCES
- 1. Аmisha, Malik P., Pathania M., Rathaur V.K. Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care. 2019. Vol. 8(7). P. 2328–2331. URL: https://doi.org/10.4103 .
- 2. Artificial Intelligence in Control and Decision-making Systems. Dedicated to Professr Janusz Kacprzyk. Kondratenko Yu.P., Kreinovich V., Pedrycz W., Chikrii A., Gil-Lafuente A.M. (Eds.). Studies in Computational Intelligence. Vol. 1087. Springer, 2023. 388 р.
- 3. Pashkov V.M., Harkusha A.O., Harkusha Y.O. Аrtificial intelligence in medical practice: Regulative issues and perspectives. Wiad Lek. 2020. Vol. 73(12 cz 2). P. 2722–2727
- 4. Kulikowski C.A. Beginnings of artificial intelligence in medicine (AIM): Computational artifice assisting scientific inquiry and clinical art with reflections on present AIM challenges. Yearb Med Inform. 2019. Vol. 28(1). P. 249–256. URL: https://doi.org/10.1055 .
- 5. Mintz Y., Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019. Vol. 28(2). P. 73–81. URL: https://doi.org/10.1080 .
- 6. Torrente A., Maccora S., Prinzi F., Alonge P., Pilati L., Lupica A., Di Stefano V., Camarda C., Vitabile S., Brighina F. The clinical relevance of artificial intelligence in migraine. Brain Sci. 2024. Vol. 14(1). P. 85. URL: https://doi.org/10.3390 .
- 7. Nawab K., Athwani R., Naeem A., Hamayun M., Wazir M. A review of applications of artificial intelligence in gastroenterology. Cureus. 2021. Vol. 13(11). e19235. URL: https://doi.org/ 10.7759 .
- 8. Ahmed N., Abbasi M.S., Zuberi F., Qamar W., Halim M.S.B., Maqsood A., Alam M.K. Artificial intelligence techniques: Analysis, application, and outcome in dentistry-A systematic review. Biomed Res Int. 2021. 9751564. URL: https://doi.org/10.1155/2021 .
- 9. Talpur S., Azim F., Rashid M., Syed S.A., Talpur B.A., Khan S.J. Uses of different machine learning algorithms for diagnosis of dental caries. J Healthc Eng. 2022. 5032435. URL: https://doi.org/10.1155/2022 .
- 10. Ramesh A.N, Kambhampati C., Monson J.R., Drew P.J. Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England. 2004. Vol. 86(5). P. 334–338. URL: https://doi.org/10.1308 .
- 11. Yang F., Chen R., Yang Y., Yang Z., Su Y., Ji M., Pang Z., Wang D. Computed tomography-based radiomics model to predict adverse clinical outcomes in acute pulmonary embolism. Journal of Thrombosis and Thrombolysis. 2024. Vol. 57. P. 428–436. URL: https://doi.org/10.1007 .
- 12. Larentzakis A., Lygeros N. Artificial intelligence (AI) in medicine as a strategic valuable tool. Pan African Medical Journal. 2021. Vol. 38. 184. URL: https://doi.org/10.11604 .
- 13. Sim J.Z.T., Fong Q.W., Huang W., Tan C.H. Machine learning in medicine: What clinicians should know. Singapore Medical Journal. 2023. Vol. 64(2). P. 91–97. URL: https://doi.org/ 10.11622 .
- 14. Liew C.J. Medicine and artificial intelligence: A strategy for the future, employing Porter’s classic framework. Singapore Medical Journal. 2020. Vol. 61(8). 447. URL: https://doi.org/ 10.11622 .
- 15. Koohy H. The rise and fall of machine learning methods in biomedical research. (Version 2; peer review: 2 approved). F1000Research. 2018. Vol. 6. 2012. URL: https://doi.org/10.12688 .
- 16. Tsai T.L., Fridsma D.B., Gatti G. Computer decision support as a source of interpretation error: The case of electrocardiograms. Journal of the American Medical Informatics Association. 2023. Vol. 10, Iss. 5. P. 478–483. URL: https://doi.org/10.1197 .
- 17. Wang H., Zu Q., Chen J., Yang Z., Ahmed M.A. Application of artificial intelligence in acute coronary syndrome: A brief literature review. 2021. Vol. 38. P. 5078–5086. URL: https://doi.org/ 10.1007 .
- 18. Miller D.D. Machine intelligence in cardiovascular medicine. Cardiology in Review. 2020. Vol. 28(2). P. 53–64. URL: https://doi.org/10.1097 .
- 19. Ng B., Nayyar S., Chauhan V.S. The role of artificial intelligence and machine learning in clinical cardiac electrophysiology. Canadian Journal of Cardiology. 2022. Vol. 38, Iss. 2. P. 246–258. URL: https://doi.org/10.1016 .
- 20. Juarez-Orozco L.E., Kl?n R., Niemi M., Ruijsink B., Daquarti G., van Es R., Benjamins J.W., Yeung M.W., van der Harst P., Knuuti J. Artificial intelligence to improve risk prediction with nuclear cardiac studies. Current Cardiology Reports. 2022. Vol. 24(4). P. 307–316. URL: https://doi.org/10.1007 .
- 21. Aung Y.Y.M., Wong D.C.S., Ting D.S.W. The promise of artificial intelligence: A review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin. 2021. Vol. 139, Iss. 1. P. 4–15. URL: https://doi.org/10.1093 .
- 22. Jassar S., Adams S.J., Zarzeczny A., Burbridge B.E. The future of artificial intelligence in medicine: Medical-legal considerations for health leaders. Healthcare Management Forum. 2022. Vol. 35, Iss. 3. P. 185–189. URL: https://doi.org/10.1177 .
- 23. Mayer M.A. Inteligencia artificial en atencin primaria: un escenario de oportunidades y desafios [Artificial intelligence in primary care: A scenario of opportunities and challenges]. Atencion Primaria. 2023. Vol. 55, Iss. 11. 102744. URL: https://doi.org/10.1016 .
- 24. Pongtriang P., Rakhab A., Bian J., Guo Y., Maitree K. Challenges in adopting artificial intelligence to improve healthcare systems and outcomes in Thailand. Healthcare Informatics Research. 2023. Vol. 29(3). P. 280–282. URL: https://doi.org/10.4258 .
- 25. Lee D., Yoon S.N. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health. 2021. Vol. 18(1). 271. URL: https://doi.org/10.3390 .
- 26. Mudgal S.K., Agarwal R., Chaturvedi J., Gaur R., Ranjan N. Real-world application, challenges and implication of artificial intelligence in healthcare: an essay. Pan African Medical Journal. 2022. Vol. 43. 3. URL: https://doi.org/10.11604 .
- 27. Vnnen A., Haataja K., Vehvilinen-Julkunen K., Toivanen P. Proposal of a novel artificial intelligence distribution service platform for healthcare. (Version 1; peer review: 2 approved). F1000Research. 2021. Vol. 10. 245. URL: https://doi.org/10.12688 .
- 28. Rogers W.A., Draper H., Carter S.M. Evaluation of artificial intelligence clinical applications: Detailed case analyses show value of healthcare ethics approach in identifying patient care issues. Bioethics. 2021. Vol. 35(7). P. 623–633. URL: https://doi.org/10.1111 .
- 29. Srivastava R. Applications of artificial intelligence in medicine. Exploratory Research and Hypothesis in Medicine. 2024. Vol. 9(2). P. 138–146. URL: https://doi.org/10.14218 .
- 30. Moor M., Banerjee O., Abad Z.S.H., Krumholz H.M., Leskovec J., Topol E.J., Rajpurkar P. Foundation models for generalist medical artificial intelligence. Nature. 2023. Vol. 616. P. 259–265. URL: https://doi.org/10.1038 .
- 31. Kolossvary M., De Cecco C.N., Feuchtner G., Maurovich-Horvat P. Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning. J Cardiovasc Comput Tomogr. 2019. Vol. 13, Iss. 5. P. 274–280. URL: https://doi.org/10.1016 .
- 32. Onopchuk Yu.N. Homeostasis of the functional respiratory system as a result of intrasystemic and system-environmental information interaction. Bioecomedicine. Unified information space. 2001. P. 59–82
- 33. Onopchuk Yu.N. Homeostasis of the functional circulatory system as a result of intrasystemic and system-environmental information interaction. Bioecomedicine. Unified information space. 2001. P. 82–104
- 34. Aralova N.I. Mathematical models of the functional respiratory system for solving applied problems of occupational medicine and sports [in Russian]. Saarbrcken: LAP LAMBERT Academic Publishing GmbH&Co, KG. 2019. 368 p.
- 35. Polinkevich K.B., Onopchuk Yu.N. Conflict situations in the regulation of the main function of the respiratory system of the body and mathematical models for their resolution. Kibernetika. 1986. N 3. P. 100–104
- 36. Filippov A.F. Differential equations with discontinuous right-hand side [in Russian]. Moscow: Nauka, 1985. 224 p.
- 37. Ermakova I.I. Temperature homeostasis as a result of intrasystemic and system-environmental information interaction. Bioecomedicine. Unified information space. 2001. P. 104–116
- 38. Marchuk G.I. The simplest mathematical model of a viral disease. Preprint. Novosibirsk, Computing Center of the USSR Academy of Sciences, 1975. 36 p.