USING BAYESIAN METHODS IN THE TASK OF MODELING THE PATIENTS' PHARMACORESISTANCE DEVELOPMENT
Mariia A. Voronenko
Kherson National Technical University (Ukraine)
http://orcid.org/0000-0002-5392-5125
Ulzhalgas M. Zhunissova
Astana Medical University (Kazakhstan)
http://orcid.org/0000-0001-5255-9314
Saule S. Smailova
D.Serikbayev East Kazakhstan State Technical University (Kazakhstan)
http://orcid.org/0000-0002-8411-3584
Luidmila N. Lytvynenko
Kherson City Psychoneurological Clinic (Ukraine)
http://orcid.org/0000-0001-8445-5704
Nataliia B. Savina
National University of Water and Environmental Engineering (Ukraine)
http://orcid.org/0000-0001-8339-1219
Pavlo P. Mulesa
Uzhhorod National University (Ukraine)
http://orcid.org/0000-0002-3437-8082
Volodymyr I. Lytvynenko
immun56@gmail.comKherson National Technical University (Ukraine)
http://orcid.org/0000-0002-1536-5542
Abstract
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.
Keywords:
epileptology, pharmacoresistance, Bayesian networks, structural learning, parametric learning, sensitivity analysis, validationReferences
Bates D. W., Kuperman G. J., Wang S., Gandhi T., Kittler A.: Ten commandments for effective clinical decision support: Making the practice of evidence-based medicine a reality. Journal of the American Medical Informatics Association 10, 2003, 523–530.
DOI: https://doi.org/10.1197/jamia.M1370
Google Scholar
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.
DOI: https://doi.org/10.1109/3468.594909
Google Scholar
Cheeseman P., Kelly M., Taylor W., Freema D., Stutz J.: Bayesian classification. Proceedings of AAAI, St. Paul 1988, 607–611.
Google Scholar
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.
DOI: https://doi.org/10.1002/asm.3150050106
Google Scholar
Darwiche A.: A differential approach to inference in Bayesian networks. Proceedings of Uncertainty in Artificial Intelligence 2000, 123–132.
Google Scholar
Hiritis N.: Predictors of pharmacoresistant epilepsy. Epilepsy research 75(2-3), 2007, 192–196.
DOI: https://doi.org/10.1016/j.eplepsyres.2007.06.003
Google Scholar
Kahane Ph., Berg A., Loscher W.: Current knowledge on basic mechanism of drug resistance. Drug resistant epilepsy, UK John Libbey Eurotext, 2008, 47–57.
Google Scholar
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.
DOI: https://doi.org/10.1136/bmj.38398.500764.8F
Google Scholar
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.
Google Scholar
Kjærulff U., van der Gaag L. C.: Making sensitivity analysis computationally efficient. Proceedings of Uncertainty in Artificial Intelligence 2000, 317–325.
Google Scholar
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.
DOI: https://doi.org/10.1111/j.1528-1167.2009.02397.x
Google Scholar
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.
DOI: https://doi.org/10.1016/S0950-7051(98)00060-4
Google Scholar
Miller R.: Medical diagnostic decision support systems-past, present and future. Journal of the American Medical Informatics Association 1, 1994, 8–27.
DOI: https://doi.org/10.1136/jamia.1994.95236141
Google Scholar
Musen M. A., Shahar Y., Shortliffe E. H.: Biomedial Informatics: computer applications in health care and biomedicine. Springer, New York 2006, 698–736.
DOI: https://doi.org/10.1007/0-387-36278-9_20
Google Scholar
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.
Google Scholar
Percell G. P.: What makes a good clinical decision support system. British Medical Journal 330, 2005, 740–741.
DOI: https://doi.org/10.1136/bmj.330.7494.740
Google Scholar
Authors
Mariia A. VoronenkoKherson National Technical University Ukraine
http://orcid.org/0000-0002-5392-5125
Authors
Ulzhalgas M. ZhunissovaAstana Medical University Kazakhstan
http://orcid.org/0000-0001-5255-9314
Authors
Saule S. SmailovaD.Serikbayev East Kazakhstan State Technical University Kazakhstan
http://orcid.org/0000-0002-8411-3584
Authors
Luidmila N. LytvynenkoKherson City Psychoneurological Clinic Ukraine
http://orcid.org/0000-0001-8445-5704
Authors
Nataliia B. SavinaNational University of Water and Environmental Engineering Ukraine
http://orcid.org/0000-0001-8339-1219
Authors
Volodymyr I. Lytvynenkoimmun56@gmail.com
Kherson National Technical University Ukraine
http://orcid.org/0000-0002-1536-5542
Statistics
Abstract views: 227PDF downloads: 139
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Svitlana A. Yaremko, Elena M. Kuzmina, Nataliia B. Savina, Iryna Yu. Yepifanova, Halyna B. Gordiichuk, Dinara Mussayeva, FORECASTING BUSINESS PROCESSES IN THE MANAGEMENT SYSTEM OF THE CORPORATION , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 12 No. 4 (2022)
- Oleksii M. Shushura, Liudmyla A. Asieieva, Oleksiy L. Nedashkivskiy, Yevhen V. Havrylko, Yevheniia O. Moroz, Saule S. Smailova, Magzhan Sarsembayev, SIMULATION OF INFORMATION SECURITY RISKS OF AVAILABILITY OF PROJECT DOCUMENTS BASED ON FUZZY LOGIC , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 12 No. 3 (2022)
- Anna Vitiuk, Leonid Polishchuk, Nataliia B. Savina, Oksana O. Adler, Gulzhan Kashaganova, Saule Kumargazhanova, ENGINEERING AND TECHNICAL ASSESSMENT OF THE COMPETITIVENESS OF UKRAINIAN MECHANICAL ENGINEERING ENTERPRISES BASED ON THE APPLICATION OF REGRESSION MODELS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 3 (2023)
- Oleksandr Harnaha, Nataliia B. Savina, Volodymyr Hrytsiuk, ENVIRONMENTAL AND ECONOMIC ASSESSMENT OF THE LAND USE REGULATION EFFECTIVENESS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 13 No. 4 (2023)
