{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:59:00Z","timestamp":1787317140169,"version":"build-2736575974"},"reference-count":19,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CPT Pharmacom &amp; Syst Pharma"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time\u2010consuming to develop. There is great interest in the adoption of machine\u2010learning methods, but most implementations cannot be reliably extrapolated to treatment strategies outside of the training data. In order to solve this problem, we propose the deep compartment model (DCM), a combination of neural networks and ordinary differential equations. Using simulated datasets of different sizes, we show that our model remains accurate when training on small data sets. Furthermore, using a real\u2010world data set of patients with hemophilia A receiving factor VIII concentrate while undergoing surgery, we show that our model more accurately predicts a priori drug concentrations compared to a previous NLME model. In addition, we show that our model correctly describes the changing drug concentration over time. By adopting pharmacokinetic principles, the DCM allows for simulation of different treatment strategies and enables therapeutic drug monitoring.<\/jats:p>","DOI":"10.1002\/psp4.12808","type":"journal-article","created":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T05:40:59Z","timestamp":1653630059000},"page":"934-945","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Deep compartment models: A deep learning approach for the reliable prediction of time\u2010series data in pharmacokinetic modeling"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0719-0831","authenticated-orcid":false,"given":"Alexander","family":"Janssen","sequence":"first","affiliation":[{"name":"Department of Clinical Pharmacology, Hospital Pharmacy Amsterdam University Medical Center  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank W. G.","family":"Leebeek","sequence":"additional","affiliation":[{"name":"Department of Hematology Erasmus University Medical Center  Rotterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marjon H.","family":"Cnossen","sequence":"additional","affiliation":[{"name":"Department of Pediatric Hematology Erasmus University Medical Center\u2010Sophia Children's Hospital  Rotterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ron A. A.","family":"Math\u00f4t","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacology, Hospital Pharmacy Amsterdam University Medical Center  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the OPTI\u2010CLOT study group and SYMPHONY consortium","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1016260720218"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1021\/js9604016"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0135784"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04167-0"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.3324\/haematol.2015.136275"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2352-3026(21)00135-6"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-005-9000-2"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2021.102804"},{"key":"e_1_2_10_10_1","article-title":"Continuously differentiable exponential linear units","author":"Barron JT","year":"2017","journal-title":"arXiv preprint arXiv:1704.07483"},{"issue":"1","key":"e_1_2_10_11_1","first-page":"5","article-title":"Swish: a self\u2010gated activation function","volume":"7","author":"Ramachandran P","year":"2017","journal-title":"arXiv Preprint arXiv:171005941"},{"key":"e_1_2_10_12_1","article-title":"Adam: a method for stochastic optimization","author":"Kingma DP","year":"2014","journal-title":"arXiv preprint arXiv:1412.6980"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/jth.12055"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2516.2010.02383.x"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365-2141.2002.3406_1.x"},{"key":"e_1_2_10_16_1","series-title":"Proceedings of Machine Learning Research","first-page":"6458","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Vladimirova M","year":"2019"},{"key":"e_1_2_10_17_1","article-title":"Output\u2010constrained bayesian neural networks","author":"Yang W","year":"2019","journal-title":"arXiv preprint arXiv:1905.06287"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2022.3155327"},{"key":"e_1_2_10_19_1","unstructured":"Janssen A. Leebeek F. W. G. Cnossen M. H. andMath\u00f4t R. A. A.(2021).The neural mixed effects algorithm: leveraging machine learning for pharmacokinetic modelling. In 29thAnnual Meeting of the Population Approach Group in Europe Abstr 9826 (https:\/\/www.page\u2010meeting.org\/?abstract=9826)."},{"key":"e_1_2_10_20_1","first-page":"4768","volume-title":"A unified approach to interpreting model predictions","author":"Lundberg SM","year":"2017"}],"container-title":["CPT: Pharmacometrics &amp; Systems Pharmacology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/psp4.12808","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/psp4.12808","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/psp4.12808","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T18:48:49Z","timestamp":1703875729000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascpt.onlinelibrary.wiley.com\/doi\/10.1002\/psp4.12808"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,27]]},"references-count":19,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["10.1002\/psp4.12808"],"URL":"https:\/\/doi.org\/10.1002\/psp4.12808","archive":["Portico"],"relation":{},"ISSN":["2163-8306","2163-8306"],"issn-type":[{"value":"2163-8306","type":"print"},{"value":"2163-8306","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,27]]},"assertion":[{"value":"2021-12-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-16","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}