USING BAYESIAN METHODS IN THE TASK OF MODELING THE PATIENTS' PHARMACORESISTANCE DEVELOPMENT

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, validation

References

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

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

Cheeseman P., Kelly M., Taylor W., Freema D., Stutz J.: Bayesian classification. Proceedings of AAAI, St. Paul 1988, 607–611.

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

Darwiche A.: A differential approach to inference in Bayesian networks. Proceedings of Uncertainty in Artiﬁcial Intelligence 2000, 123–132.

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

Kahane Ph., Berg A., Loscher W.: Current knowledge on basic mechanism of drug resistance. Drug resistant epilepsy, UK John Libbey Eurotext, 2008, 47–57.

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

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.

Kjærulff U., van der Gaag L. C.: Making sensitivity analysis computationally efﬁcient. Proceedings of Uncertainty in Artiﬁcial Intelligence 2000, 317–325.

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

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

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

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

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.

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

Voronenko, M. A., Zhunissova, U. M., Smailova, S. S., Lytvynenko, L. N., Savina, N. B., Mulesa, P. P., & Lytvynenko, V. I. (2022). USING BAYESIAN METHODS IN THE TASK OF MODELING THE PATIENTS’ PHARMACORESISTANCE DEVELOPMENT. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(2), 77–82. https://doi.org/10.35784/iapgos.2968

Article Sidebar

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License