Problems of Social Hygiene, Public Health and History of MedicineProblems of Social Hygiene, Public Health and History of Medicine0869-866X2412-2106Joint-Stock Company Chicot160610.32687/0869-866X-2025-33-2-263-272НеопределенThe development of model of prognostication and minimization of risk of by-effects under combined application of agents for treatment of chronic cardiac deficiency using AIKuropatkinaT A0sylphide0@gmail.comSivakovaT Vsivakova15@mail.ruShegaiM MMshegai@yandex.ruOrlovYu Norlmath@keldysh.ruShimanovskiiN Lshimannn@yandex.ruThe Federal State Budget Educational Institution of Higher Education “The G. V. Plekhanov Russian Economic University”, 115054, Moscow, RussiaThe Federal State Institution “The M. V. Keldysh Federal Research Center the Institute of Applied Mathematics of the Russian Academy of Sciences”, 125047, Moscow, RussiaN. A. Semashko National Research Institute of Public Health, 105064, Moscow, RussiaThe Federal State Budget Educational Institution of Higher Education “N. I. Pirogov Russian National Research Medical University” of Minzdrav of Russia, 117997, Moscow, Russia290420253321005202510052025Copyright © 2025,2025<p>The chronic cardiac deficiency continues to be one of the leading health care problems requiring innovative solutions. The article presents mathematical algorithm to evaluate drug interactions and targeted to minimize side effects and to optimize chronic cardiac deficiency therapy. The mathematical model, elaborated using AI, is based on analysis of fully connected sub-graphs and ranking of side effects of combined application of medications. This approach permits to implement optimal selection of the safest and most effective combinations of medications. This is particularly important with regard for co-morbid conditions when patients take simultaneously several different medications. The proposed approach can significantly improve risk prediction and favor more precise selection of combined therapy. The algorithm surmises necessity for further extension and specification of model, including consideration of wider spectrum of medications and mechanism of their interaction. In the context of rapidly advancing digital medicine, models based on mathematical algorithms and machine learning can complement systems of clinical decision support. 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