{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T09:47:09Z","timestamp":1778320029523,"version":"3.51.4"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100005860","name":"Helsefonden","doi-asserted-by":"publisher","award":["20\u2010B\u20100059"],"award-info":[{"award-number":["20\u2010B\u20100059"]}],"id":[{"id":"10.13039\/501100005860","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Fonden","doi-asserted-by":"publisher","award":["NNF15SA0018404"],"award-info":[{"award-number":["NNF15SA0018404"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Pharma and Therapeutics"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:p>We compared the predictive performance of an artificial neural network to traditional pharmacometric modeling for population prediction of plasma concentrations of valproate in real\u2010world data. We included individuals aged 65\u00a0years or older with epilepsy who redeemed their first prescription of valproate after the diagnosis of epilepsy and had at least one valproate plasma concentration measured. A long short\u2010term memory neural network (LSTM) was developed using the training data set to fit the LSTM and the test data set to validate the model. Predictions from the LSTM were compared with those obtained from the population predictions from a pharmacometric model by Birnbaum <jats:italic>et al<\/jats:italic>. which had the best predictive performance for population predictions of valproate concentrations in Danish databases. We used the cutoff of \u00b1\u00a020\u00a0mg\/L of prediction error to define good predictions. A total of 1,252 individuals were included in the study. The LSTM fitted using the training data set had poor predictive performance in the test data set, but better than that of the pharmacometric model. The proportion of individuals with at least one predicted concentration within \u00b1\u00a020\u00a0mg\/L of observed concentration was largest in case of the LSTM (64.4%, 95% confidence interval (CI): 58.4\u201370.2%) compared with the pharmacometric model by Birnbaum <jats:italic>et al<\/jats:italic>. (49.8%, 95% CI: 47.0\u201352.6%). LSTM shows better predictive performance to predict valproate plasma concentrations compared with a traditional pharmacometric model in the investigated setting with real\u2010world data in older patients with epilepsy where information on exact timepoints for both dosing and plasma concentration measurement are missing.<\/jats:p>","DOI":"10.1002\/cpt.2577","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T17:19:43Z","timestamp":1646846383000},"page":"1278-1285","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Artificial Neural Network vs. Pharmacometric Model for Population Prediction of Plasma Concentration in Real\u2010World Data: A Case Study on Valproic Acid"],"prefix":"10.1111","volume":"111","author":[{"given":"Hiie","family":"Soeorg","sequence":"first","affiliation":[{"name":"Pharmacovigilance Research Center Department of Drug Design and Pharmacology University of Copenhagen Copenhagen Denmark"},{"name":"Pharmacometrics Research Group Department of Drug Design and Pharmacology University of Copenhagen Copenhagen Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eva","family":"Sverrisd\u00f3ttir","sequence":"additional","affiliation":[{"name":"Pharmacometrics Research Group Department of Drug Design and Pharmacology University of Copenhagen Copenhagen Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Morten","family":"Andersen","sequence":"additional","affiliation":[{"name":"Pharmacovigilance Research Center Department of Drug Design and Pharmacology University of Copenhagen Copenhagen Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trine Meldgaard","family":"Lund","sequence":"additional","affiliation":[{"name":"Pharmacometrics Research Group Department of Drug Design and Pharmacology University of Copenhagen Copenhagen Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maurizio","family":"Sessa","sequence":"additional","affiliation":[{"name":"Pharmacovigilance Research Center Department of Drug Design and Pharmacology University of Copenhagen Copenhagen Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.2502"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpdd.809"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0000000000004897"},{"key":"e_1_2_10_5_1","volume-title":"R: A language and environment for statistical computing","author":"R Core Team","year":"2021"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/FTD.0b013e31811f3296"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S245060"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00228-021-03188-9"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2021.102804"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.9740\/mhc.n106822"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eplepsyres.2004.08.009"},{"key":"e_1_2_10_13_1","unstructured":"Allaire J. &Chollet F.keras: R Interface to 'Keras'. 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