{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T14:05:25Z","timestamp":1775829925859,"version":"3.50.1"},"reference-count":23,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004564","name":"Ministarstvo Prosvete, Nauke i Tehnolo\u0161kog Razvoja","doi-asserted-by":"publisher","award":["451-03-136\/2025-03\/200109"],"award-info":[{"award-number":["451-03-136\/2025-03\/200109"]}],"id":[{"id":"10.13039\/501100004564","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_bmt-2025-0491_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This paper presents an experimental numerical method for modeling and analyzing stochastic systems. For this purpose, various machine prediction models are trained using the Monte Carlo simulation method. This method is presented using experimental data of a kidney transplantation with an immunosuppressive protocol based on tacrolimus.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2025-0491_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A multivariate regression model was constructed by previous authors based on a clinical study in which key independent physiological parameters such as serum creatinine and estimated glomerular filtration rate (eGFR) six months after transplantation, as well as the pharmacokinetics of tacrolimus, including the dose-adjusted trough concentration of tacrolimus (C0\/D) and intrastation variability (IPV), and eGFR between 13 and 36 were the dependent variable. Using the Monte Carlo simulation method, this model is further applied to obtain the essential data for the optimization of the prediction models. To determine the optimal prediction model, the DecisionTreeClassifier, Random Forest Classifier, and XGBClassifier were trained and compared.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2025-0491_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The results indicate that XGBoost is the most accurate, reliable and generalizable model among the classifiers tested, while Monte Carlo simulation represents a significant methodological advance in the field of kidney transplantation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2025-0491_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Advanced numerical methods for kidney transplant patients\u2019 therapy are step forward in optimization of current immunosuppressive protocols.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2025-0491","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T14:00:11Z","timestamp":1769004011000},"page":"129-135","source":"Crossref","is-referenced-by-count":0,"title":["Numerical modeling and prediction of late estimated glomerular filtration rate in kidney transplant recipients based on machine learning models and the Monte Carlo simulation method"],"prefix":"10.1515","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0062-4342","authenticated-orcid":false,"given":"Ivan R.","family":"Pavlovi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering , 112400 University of Ni\u0161 , Ni\u0161 , Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2599-7508","authenticated-orcid":false,"given":"Nikola","family":"Stefanovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Medicine , University of Ni\u0161 , Ni\u0161 , Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4387-1688","authenticated-orcid":false,"given":"Nikola","family":"Despeni\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering , 112400 University of Ni\u0161 , Ni\u0161 , Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6048-4834","authenticated-orcid":false,"given":"Dragana R.","family":"Pavlovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Medicine , University of Ni\u0161 , Ni\u0161 , Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1923-2971","authenticated-orcid":false,"given":"Ma\u0161a","family":"Jovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Medicine , University of Ni\u0161 , Ni\u0161 , Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9055-3378","authenticated-orcid":false,"given":"Radmila","family":"Velickovi\u0107-Radovanovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Medicine , University of Ni\u0161 , Ni\u0161 , Serbia"},{"name":"University Clinical Centre of Ni\u0161 , Ni\u0161 , Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9370-8865","authenticated-orcid":false,"given":"Branka","family":"Miti\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Medicine , University of Ni\u0161 , Ni\u0161 , Serbia"},{"name":"University Clinical Centre of Ni\u0161 , Ni\u0161 , Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6616-4380","authenticated-orcid":false,"given":"Tatjana P.","family":"Cvetkovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Medicine , University of Ni\u0161 , Ni\u0161 , Serbia"},{"name":"University Clinical Centre of Ni\u0161 , Ni\u0161 , Serbia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"2026041013241372422_j_bmt-2025-0491_ref_001","doi-asserted-by":"crossref","unstructured":"Thongprayoon, C, Hansrivijit, P, Kovvuru, K, Kanduri, SR, Bathini, T, Pivovarova, A, et al.. Impacts of high intra-and inter-individual variability in tacrolimus pharmacokinetics and fast tacrolimus metabolism on outcomes of solid organ transplant recipients. J\u00a0Clin Med 2020;9:2193. https:\/\/doi.org\/10.3390\/jcm9072193.","DOI":"10.3390\/jcm9072193"},{"key":"2026041013241372422_j_bmt-2025-0491_ref_002","doi-asserted-by":"crossref","unstructured":"van Gelder, T, Meziyerh, S, Swen, JJ, de Vries, APJ, Moes, DJAR. The clinical impact of the C0\/D ratio and the CYP3A5 genotype on outcome in tacrolimus treated kidney transplant recipients. Front Pharmacol 2020;11:1142. https:\/\/doi.org\/10.3389\/fphar.2020.01142.","DOI":"10.3389\/fphar.2020.01142"},{"key":"2026041013241372422_j_bmt-2025-0491_ref_003","doi-asserted-by":"crossref","unstructured":"Marco, DN, Salas, MQ, Guti\u00e9rrez-Garc\u00eda, G, Monge, I, Riu, G, Carcelero, E, et al.. 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Pharmaceutics 2021;13:1970. https:\/\/doi.org\/10.3390\/pharmaceutics13111970.","DOI":"10.3390\/pharmaceutics13111970"},{"key":"2026041013241372422_j_bmt-2025-0491_ref_015","doi-asserted-by":"crossref","unstructured":"Cati\u0107-\u0110or\u0111evi\u0107, A, Pavlovi\u0107, I, Spasi\u0107, A, Stefanovi\u0107, N, Pavlovi\u0107, D, Damnjanovi\u0107, I, et al.. Assessment of pharmacokinetic mycophenolic acid clearance models using Monte Carlo numerical analysis. Xenobiotica 2021;51:387\u201393. https:\/\/doi.org\/10.1080\/00498254.2020.1871532.","DOI":"10.1080\/00498254.2020.1871532"},{"key":"2026041013241372422_j_bmt-2025-0491_ref_016","unstructured":"Pedregosa, F, Varoquaux, G, Gramfort, A, Michel, V, Thirion, B, Grisel, O, et al.. Scikit-learn: machine learning in python. J\u00a0Mach Learn Res 2011;12:2825\u201330."},{"key":"2026041013241372422_j_bmt-2025-0491_ref_017","unstructured":"Arshed, N. Type I and type II errors: Monte Carlo simulations. Noman Arshed\u2019s Blog; 2013. 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