Using Bayesian methods in the task of modeling the patients' pharmacoresistance development

• Autorzy: Voronenko Mariia A. , Zhunissova Ulzhalgas M. , Smailova Saule S. , Lytvynenko Luidmila N. , Savina Nataliia B. , Mulesa Pavlo P. , Lytvynenko Volodymyr

Identyfikatory

DOI

10.35784/iapgos.2968

Warianty tytułu

PL

Zastosowanie metod bayesowskich do modelowania rozwoju farmakooporności u pacjentów

• Języki publikacji: EN

Abstrakty

EN

In this paper, we propose a methodology for using static Bayesian networks (BN) in modeling the development of pharmacoresistance in patients with a diagnosis of epilepsy. Methods for constructing the structure of a static BN, their parametric training, validation, sensitivity analysis and “What-if” scenario analysis are considered. The model was designed in collaboration with expert doctors, as well as expert pharmacologists in the selection and quantification of input and output variables.

PL

W niniejszej pracy zaproponowano metodologię wykorzystania statycznych sieci bayesowskich (BN) w modelowaniu rozwoju farmakooporności u pacjentów z rozpoznaniem padaczki. Rozważane są metody konstruowania struktury statycznej BN, jej parametrycznego treningu, walidacji, analizy wrażliwości i analizy scenariuszy "co-jeśli". Model został zaprojektowany we współpracy z ekspertami – lekarzami, a także ekspertami – farmakologami w zakresie doboru i kwantyfikacji zmiennych wejściowych i wyjściowych.

Słowa kluczowe

EN

epileptology; pharmacoresistance; Bayesian networks; structural learning; parametric learning; sensitivity analysis; validation

PL

epileptologia; farmakooporność; sieci bayesowskie; uczenie strukturalne; uczenie parametryczne; analiza wrażliwości; walidacja

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2022
• Tom: T. 12, nr 2
• Strony: 77--82
• Opis fizyczny: Bibliogr. 16 poz., tab., wykr.

Twórcy

autor

Voronenko Mariia A.

• Kherson National Technical University, Kherson, Ukraine

• https://orcid.org/0000-0002-5392-5125

autor

Zhunissova Ulzhalgas M.

• Astana Medical University, Astana, Kazakhstan

• https://orcid.org/0000-0001-5255-9314

autor

Smailova Saule S.

• D.Serikbayev East Kazakhstan State Technical University, Ust-Kamenogorsk, Kazakhstan

• https://orcid.org/0000-0002-8411-3584

autor

Lytvynenko Luidmila N.

• Kherson City Psychoneurological Clinic, Kherson, Ukraine

• https://orcid.org/0000-0001-8445-5704

autor

Savina Nataliia B.

• National University of Water and Environmental Engineering, Rivne, Ukraine

• https://orcid.org/0000-0001-8339-1219

autor

Mulesa Pavlo P.

• Uzhhorod National University, Uzhhorod, Ukraine

• https://orcid.org/0000-0002-3437-8082

autor

Lytvynenko Volodymyr

• Kherson National Technical University, Kherson, Ukraine

• https://orcid.org/0000-0002-1536-5542

Bibliografia

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• [2] Castillo E. F., Guti´errez J. M., Hadi A. S.: Sensitivity analysis in discrete Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans 27(4), 1997, 412–423.
• [3] Cheeseman P., Kelly M., Taylor W., Freema D., Stutz J.: Bayesian classification. Proceedings of AAAI, St. Paul 1988, 607–611.
• [4] Cooper G. F.: Current research directions in the development of expert systems based on belief networks. Applied Stochastic Models and Data Analysis 5, 1989, 39–52.
• [5] Darwiche A.: A differential approach to inference in Bayesian networks. Proceedings of Uncertainty in Artificial Intelligence 2000, 123–132.
• [6] Hiritis N.: Predictors of pharmacoresistant epilepsy. Epilepsy research 75(2-3), 2007, 192–196.
• [7] Kahane Ph., Berg A., Loscher W.: Current knowledge on basic mechanism of drug resistance. Drug resistant epilepsy, UK John Libbey Eurotext, 2008, 47–57.
• [8] Kawamoto K., Houlihan C. A., Balas E. A., Lobach D. F.: Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. British Medical Journa 330, 2005, 765–773.
• [9] Kipersztok O., Wang H.: Another look at sensitivity of Bayesian networks to imprecise probabilities. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics 2001, 226–232.
• [10] Kjærulff U., van der Gaag L. C.: Making sensitivity analysis computationally efficient. Proceedings of Uncertainty in Artificial Intelligence 2000, 317–325.
• [11] Kwan P., Arzimanoglou A., Berg A. T., Brodie M. J.: Definition of drug resistant epilepsy: Consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies. Epilepsia 51(6), 2010, 1069–1077.
• [12] Lucas P. J. F., Boot H., Taal B. G.: Decision-theoretic network approach to treatment management and prognosis. Knowledge-based Systems 11, 1998, 321–330.
• [13] Miller R.: Medical diagnostic decision support systems-past, present and future. Journal of the American Medical Informatics Association 1, 1994, 8–27.
• [14] Musen M. A., Shahar Y., Shortliffe E. H.: Biomedial Informatics: computer applications in health care and biomedicine. Springer, New York 2006, 698–736.
• [15] Osheroff J. A.: Improving medication use and outcomes with clinical decision support: a step-by-step guide. Healthcare Information and Management Systems Society, Chicago 2009.
• [16] Percell G. P.: What makes a good clinical decision support system. British Medical Journal 330, 2005, 740–741.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).