O.D. Samorukova1

1 FSBEI HE "Ryazan State Radio Engineering University named after V.F. Utkin" (Ryazan, Russia)

1 samorukova.od@yandex.ru

Currently, the problem of rational use of drugs in the treatment of patients is acute. According to the World Health Organization, about 50% of medicines are prescribed and used inappropriately, or their intake patterns are not clear to patients, and they are not taken correctly. At the same time, the market for medicines is constantly evolving, and tracking market changes takes a lot of time for specialists and doctors. In medical systems, support for medical decision-making in terms of determining patient treatment regimens and choosing specific medications can simultaneously solve both of these problems.

The use of a medical decision support system in the selection of patient treatment regimens will make it possible to select personalized effective treatment regimens for each patient, as well as reduce the number of errors associated with the determination of the dosage regimen and inter-drug interaction.

Goal – to develop a system for supporting medical decision-making in terms of choosing a drug treatment regimen for patients.

The process of managing drug treatment for patients, the features of prescribing and choosing treatment regimens for patients are considered, and an algorithm for choosing treatment regimens based on a fuzzy decision tree is developed, a decision tree based on the generated criteria is presented, and weighting factors for treatment regimens are calculated according to the specified parameters of a specific patient's model.

The use of a system for supporting medical decision-making in choosing treatment regimens for patients will allow selecting personalized effective treatment regimens for each patient, as well as reducing the number of errors associated with determining the dosage regimen and drug interactions.

Samorukova O.D. Choosing a treatment regimen based on a fuzzy decision tree when automating the process of managing drug treatment of patients in medical systems. Biomedicine Radioengineering. 2025. V. 28. № 4. Р. 102-110. DOI: https://doi.org/10.18127/ j15604136-202504-12 (In Russian).

1. Promoting rational use of medicines. World Health Organization URL: https://www.who.int/activities/promoting-rational-use-of-medicines (data obrashcheniya: 10.05.2025).
2. Kroshilin A.V., Kroshilina S.V., Ovechkin G.V. Predmetno-orientirovannye informacionnye sistemy: Ucheb. posobie (Estestvennye nauki). M.: KURS. 2023. 176 s. (in Russian).
3. Samorukova O.D., Kroshilin A.V., Kroshilina S.V. Klyuchevye aspekty razrabotki sistemy podderzhki prinyatiya reshenij pri podbore skhemy medikamentoznogo lecheniya. Biotekhnicheskie, medicinskie i ekologicheskie sistemy, izmeritel'nye ustrojstva i robototekhnicheskie kompleksy – Biomedsistemy-2023: Sb. trudov XXXVI Vseros. nauch.-tekhn. konf. stud., mol. uchenyh i spec. pod obshch. red. V.I. Zhuleva. Ryazan': IP Konyahin A.V. (Book Jet). 2023. 332 s. S. 181–184. (in Russian).
4. Zhuleva S.YU., Kroshilin A.V., Kroshilina S.V., Samorukova O.D. Zadachi razrabotki sistem medicinskogo naznacheniya pri vybore skhemy medikamentoznogo lecheniya. Vestnik RGRTU. 2024. № 88. S. 106–114. (in Russian).
5. Perepelkin D.A., Popova A.A., Kroshilin A.V., Kroshilina S.V. Komp'yuternoe modelirovanie processov podderzhki prinyatiya reshenij vracha-stomatologa na osnove semanticheskih setej. Vestnik RGRTU. 2024. № 89. S. 127–140. (in Russian).
6. Zhulev V.I., Kroshilin A.V., Kroshilina S.V. Formirovanie znanij i struktura medicinskoj ekspertnoj sistemy. Biomedicinskaya radioelektronika. 2023. T. 26. № 3. S. 44–54. (in Russian).
7. Samorukova O.D., Kroshilin A.V., Kroshilina S.V., Ovechkin G.V. Modelirovanie processov upravleniya v organizacionnyh sistemah na osnove teorii nechetkih kognitivnyh kart. Vestnik RGRTU. № 91. 2025. S. 64–75. (in Russian).
8. Popova A.A., Kroshilin A.V., Kroshilina S.V. Intellektual'naya podderzhka prinyatiya upravlencheskih reshenij v organizacionnyh sistemah raspredeleniya zadach mezhdu sotrudnikami. Sovremennye naukoemkie tekhnologii. 2024. № 12. S. 55–60. (in Russian).
9. Milevski I. Prinyatie reshenij pri naznachenii lekarstvennyh sredstv – kratko s tochki zreniya vnutrennih boleznej – [Elektronnyj resurs] – URL: https://meduniver.com/Medical/vnutrennie_bolezni/naznachenie_lekarstv.html (data obrashcheniya: 10.05.2025). (in Russian).
10. Alcala R., Alcala-Fdez J., Casillas J. et al. Hybrid LearningModels to Get the Interpretability-Accuracy Trade-Off in Fuzzy Modeling. Soft Computing. 2006. №10. P. 717–734.
11. Car'kov S.V. Nechetkie derev'ya reshenij – [Elektronnyj resurs] – URL: https://basegroup.ru/community/articles/fuzzy-dtrees (data obrashcheniya: 10.05.2025). (in Russian).