{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T08:06:52Z","timestamp":1790237212284,"version":"4.1.0"},"reference-count":44,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"vor","delay-in-days":22,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Translational Sci"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Population pharmacokinetic (PK) models are commonly used to predict drug concentrations, but artificial intelligence (AI) models have gained interest due to their ability to identify complex patterns without requiring mathematical assumptions. This study compares the predictive performance of AI and population PK models using therapeutic drug monitoring (TDM) records of four antiepileptic drugs (AEDs): carbamazepine (CBZ), phenobarbital (PHB), phenytoin (PHE), and valproic acid (VPA). Additionally, we analyzed key covariates influencing drug concentration predicting using the most accurate model. We extracted concentration data for CBZ, PHB, PHE, and VPA from TDM reports at Seoul National University Hospital (2010\u20132021), along with patient diagnoses and lab results. The predictive performances of 10 AI models, including ensemble and deep learning models, were compared with published population PK models. The predictive performance of AI models generally exceeded that of population PK models. The best\u2010performing AI models, such as Adaboost, eXtreme Gradient Boosting, and Random Forest, had lower root mean squared error values for CBZ, PHB, PHE, and VPA (2.71, 27.45, 4.15, and 13.68\u2009\u03bcg\/mL, respectively) compared to population PK models (3.09, 26.04, 16.12, and 25.02\u2009\u03bcg\/mL). The most influential covariate was time after last drug administration. AI models, particularly ensemble methods, showed strong predictive performance and may support individualized AED dosing, improving therapeutic outcomes while minimizing adverse effects.<\/jats:p>","DOI":"10.1111\/cts.70353","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T11:15:07Z","timestamp":1761218107000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Comparison of\n                    <scp>AI<\/scp>\n                    and Population\n                    <scp>PK<\/scp>\n                    Models to Predict the Concentrations of Antiepileptic Drugs Using Therapeutic Drug Monitoring Records"],"prefix":"10.1111","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1231-4280","authenticated-orcid":false,"given":"Tae Kyu","family":"Chung","sequence":"first","affiliation":[{"name":"Department of Applied Bioengineering, Graduate School of Convergence Science and Technology Seoul National University  Seoul South Korea"},{"name":"Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine University of California\u2010San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6713-5418","authenticated-orcid":false,"given":"Howard","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology Seoul National University  Seoul South Korea"},{"name":"Department of Clinical Pharmacology and Therapeutics Seoul National University Hospital  Seoul South Korea"},{"name":"Advanced Institute of Convergence Technology  Suwon South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/14740338.2021.1865913"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.5698\/1535-7597.18.4s1.1"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pharmthera.2019.05.010"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.farma.2010.10.005"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/FTD.0b013e31825ee389"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.13400"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.56808\/3027-7922.2563"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.14803"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00228-020-03011-x"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.13510"},{"issue":"10","key":"e_1_2_9_12_1","first-page":"653","article-title":"A Systematic Review of Population Pharmacokinetics of Carbamazepine","volume":"11","author":"Methaneethorn J.","year":"2020","journal-title":"Systematic Reviews in Pharmacy"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/FTD.0000000000000354"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/psp.2013.14"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43440\u2010022\u201000445\u20101"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.2174\/1573409914666180525124608"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-85157-x"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpdd.809"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101898"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2021.102804"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105151"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/pharmaceutics14091814"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104166"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000005062"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1097\/FTD.0000000000001078"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.999548"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2010.16.4.299"},{"key":"e_1_2_9_28_1","unstructured":"L.Buitinck G.Louppe M.Blondel et\u00a0al. \u201cAPI Design for Machine Learning Software: Experiences From the Scikit\u2010Learn Project \u201d arXiv Preprint arXiv:13090238 2013."},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.07.008"},{"key":"e_1_2_9_30_1","unstructured":"D. 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