{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T19:52:06Z","timestamp":1764100326567,"version":"3.46.0"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:00Z","timestamp":1760140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the accuracy, computational cost, and portability of a new natural language processing (NLP) method for extracting medication information from clinical narratives.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We propose an original transformer-based architecture for the extraction of entities and their relations pertaining to patients\u2019 medication regimen. First, we used this approach to train and evaluate a model on French clinical notes, using a newly annotated corpus from H\u00f4pitaux Universitaires de Strasbourg. Second, the portability of the approach was assessed by conducting an evaluation on clinical documents in English from the 2018 n2c2 shared task. Information extraction accuracy and computational cost were assessed by comparison with an available method using transformers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed architecture achieves on the task of relation extraction itself performance that are competitive with the state-of-the-art on both French and English (F-measures 0.82 and 0.96 vs 0.81 and 0.95), but reduces the computational cost by 10. End-to-end (Named Entity recognition and Relation Extraction) F1 performance is 0.69 and 0.82 for French and English corpus.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>While an existing system developed for English notes was deployed in a French hospital setting with reasonable effort, we found that an alternative architecture offered end-to-end drug information extraction with comparable extraction performance and lower computational impact for both French and English clinical text processing, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed architecture can be used to extract medication information from clinical text with high performance and low computational cost and consequently suits with usually limited hospital IT resources.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf113","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T12:12:58Z","timestamp":1760011978000},"page":"1855-1864","source":"Crossref","is-referenced-by-count":0,"title":["Efficient extraction of medication information from clinical notes: an evaluation in 2 languages"],"prefix":"10.1093","volume":"32","author":[{"given":"Thibaut","family":"Fabacher","sequence":"first","affiliation":[{"name":"Service de Sant\u00e9 Publique, University Hospital of Strasbourg , Strasbourg, 67000,","place":["France"]},{"name":"IMAGeS, ICube Laboratory, Universit\u00e9 de Strasbourg, CNRS, UMR 7357 , Strasbourg, 67000,","place":["France"]},{"name":"Inria, Inserm, Universit\u00e9 Paris Cit\u00e9 , Paris, 75013,","place":["France"]}]},{"given":"Erik-Andr\u00e9","family":"Sauleau","sequence":"additional","affiliation":[{"name":"Service de Sant\u00e9 Publique, University Hospital of Strasbourg , Strasbourg, 67000,","place":["France"]},{"name":"IMAGeS, ICube Laboratory, Universit\u00e9 de Strasbourg, CNRS, UMR 7357 , Strasbourg, 67000,","place":["France"]}]},{"given":"Emmanuelle","family":"Arcay","sequence":"additional","affiliation":[{"name":"Service de Sant\u00e9 Publique, University Hospital of Strasbourg , Strasbourg, 67000,","place":["France"]}]},{"given":"Bineta","family":"Faye","sequence":"additional","affiliation":[{"name":"Service de Sant\u00e9 Publique, University Hospital of Strasbourg , Strasbourg, 67000,","place":["France"]}]},{"given":"Maxime","family":"Alter","sequence":"additional","affiliation":[{"name":"Service de Sant\u00e9 Publique, University Hospital of Strasbourg , Strasbourg, 67000,","place":["France"]}]},{"given":"Archia","family":"Chahard","sequence":"additional","affiliation":[{"name":"Service de Sant\u00e9 Publique, University Hospital of Strasbourg , Strasbourg, 67000,","place":["France"]}]},{"given":"Nathan","family":"Miraillet","sequence":"additional","affiliation":[{"name":"Service de Sant\u00e9 Publique, University Hospital of Strasbourg , Strasbourg, 67000,","place":["France"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1466-062X","authenticated-orcid":false,"given":"Adrien","family":"Coulet","sequence":"additional","affiliation":[{"name":"Inria, Inserm, Universit\u00e9 Paris Cit\u00e9 , Paris, 75013,","place":["France"]}]},{"given":"Aur\u00e9lie","family":"N\u00e9v\u00e9ol","sequence":"additional","affiliation":[{"name":"CNRS, LISN, Universit\u00e9 Paris-Saclay , Orsay, 91405,","place":["France"]}]}],"member":"286","published-online":{"date-parts":[[2025,10,11]]},"reference":[{"key":"2025112514464702100_ocaf113-B1","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1016\/j.jbi.2009.08.007","article-title":"What can natural language processing do for clinical decision support?","volume":"42","author":"Demner-Fushman","year":"2009","journal-title":"J Biomed Inform"},{"key":"2025112514464702100_ocaf113-B2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s12911-017-0537-y","article-title":"A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease","volume":"17","author":"Escudi\u00e9","year":"2017","journal-title":"BMC Med Inform Decision Making"},{"key":"2025112514464702100_ocaf113-B3","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1136\/jamia.2010.003947","article-title":"Extracting medication information from clinical text","volume":"17","author":"Uzuner","year":"2010","journal-title":"J Am Med Inform Assoc"},{"key":"2025112514464702100_ocaf113-B4","doi-asserted-by":"crossref","first-page":"104603","DOI":"10.1016\/j.jbi.2024.104603","article-title":"Extracting adverse drug events from clinical notes: a systematic review of approaches used","volume":"151","author":"Modi","year":"2024","journal-title":"J Biomed Inform"},{"key":"2025112514464702100_ocaf113-B5","doi-asserted-by":"crossref","first-page":"105122","DOI":"10.1016\/j.ijmedinf.2023.105122","article-title":"Clinical named entity recognition and relation extraction using natural language processing of medical free text: a systematic review","volume":"177","author":"Fraile Navarro","year":"2023","journal-title":"Int J Med Inform"},{"key":"2025112514464702100_ocaf113-B6","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/s10115-022-01779-1","article-title":"Information extraction from electronic medical documents: state of the art and future research directions","volume":"65","author":"Landolsi","year":"2023","journal-title":"Knowl Inform Syst"},{"key":"2025112514464702100_ocaf113-B7","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Devlin","year":"2019"},{"year":"2018","author":"Radford","key":"2025112514464702100_ocaf113-B8"},{"key":"2025112514464702100_ocaf113-B9","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.18653\/v1\/2024.semeval-1.265","volume-title":"Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)","author":"Gema","year":"2024"},{"key":"2025112514464702100_ocaf113-B10","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1093\/jamia\/ocx039","article-title":"Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings","volume":"24","author":"Carrell","year":"2017","journal-title":"J Am Med Inform Assoc"},{"key":"2025112514464702100_ocaf113-B11","first-page":"105","volume-title":"Proceedings of the Second Workshop on Domain Adaptation for NLP","author":"Miller","year":"2021"},{"key":"2025112514464702100_ocaf113-B12","first-page":"86","volume-title":"36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics","author":"Baker","year":"1998"},{"key":"2025112514464702100_ocaf113-B13","doi-asserted-by":"crossref","first-page":"e115","DOI":"10.1158\/0008-5472.CAN-17-0615","article-title":"DeepPhe: a natural language processing system for extracting cancer phenotypes from clinical records","volume":"77","author":"Savova","year":"2017","journal-title":"Cancer Res"},{"key":"2025112514464702100_ocaf113-B14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1093\/jamia\/ocz166","article-title":"2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records","volume":"27","author":"Henry","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"2025112514464702100_ocaf113-B15","first-page":"200244","article-title":"A survey on relation extraction","volume":"19","author":"Detroja","year":"2023","journal-title":"Intell Syst Appl"},{"key":"2025112514464702100_ocaf113-B16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3674501","article-title":"A comprehensive survey on relation extraction: recent advances and new frontiers","volume":"56","author":"Zhao","year":"2024","journal-title":"ACM Comput Surv"},{"key":"2025112514464702100_ocaf113-B17","first-page":"50","volume-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Zhong","year":"2021"},{"key":"2025112514464702100_ocaf113-B18","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.18653\/v1\/P17-1113","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"Zheng","year":"2017"},{"key":"2025112514464702100_ocaf113-B19","doi-asserted-by":"crossref","first-page":"6003","DOI":"10.18653\/v1\/2024.findings-emnlp.348","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2024","author":"Mtumbuka","year":"2024"},{"key":"2025112514464702100_ocaf113-B20","first-page":"420","article-title":"Extracting adverse drug events from clinical notes","volume":"2021","author":"Mahendran","year":"2021","journal-title":"AMIA Jt Summits Transl Sci Proc"},{"key":"2025112514464702100_ocaf113-B21","first-page":"1236","article-title":"Relation extraction from clinical narratives using pre-trained language models","volume":"2019","author":"Wei","year":"2020","journal-title":"AMIA Annu Symp Proc"},{"year":"2021","author":"Yang","key":"2025112514464702100_ocaf113-B22"},{"year":"2019","author":"Huang","key":"2025112514464702100_ocaf113-B23"},{"key":"2025112514464702100_ocaf113-B24","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"2025112514464702100_ocaf113-B25","first-page":"2692","volume-title":"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)","author":"Touchent","year":"2024"},{"volume-title":"Extraction and Normalization of Simple and Structured Entities in Medical Documents","year":"2021","author":"Wajsb\u00fcrt","key":"2025112514464702100_ocaf113-B26"},{"year":"2015","author":"Abadi","key":"2025112514464702100_ocaf113-B27"},{"key":"2025112514464702100_ocaf113-B28","doi-asserted-by":"publisher","first-page":"btae681","DOI":"10.1093\/bioinformatics\/btae681","article-title":"Facilitating phenotyping from clinical texts: the medkit library","volume":"40","author":"Neuraz","year":"2024","journal-title":"Bioinformatics"},{"key":"2025112514464702100_ocaf113-B29","first-page":"8024","volume-title":"Adv Neural Inform Process Syst","author":"Paszke","year":"2019"},{"key":"2025112514464702100_ocaf113-B30","doi-asserted-by":"crossref","first-page":"38","DOI":"10.18653\/v1\/2020.emnlp-demos.6","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations","author":"Wolf","year":"2020"},{"key":"2025112514464702100_ocaf113-B31","doi-asserted-by":"crossref","first-page":"56","DOI":"10.25080\/Majora-92bf1922-00a","volume-title":"Proceedings of the 9th Python in Science Conference","author":"McKinney","year":"2010"},{"key":"2025112514464702100_ocaf113-B32","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"year":"2018","author":"Nakayama","key":"2025112514464702100_ocaf113-B33"},{"key":"2025112514464702100_ocaf113-B34","doi-asserted-by":"crossref","first-page":"2100707","DOI":"10.1002\/advs.202100707","article-title":"Green algorithms: quantifying the carbon footprint of computation","volume":"8","author":"Lannelongue","year":"2021","journal-title":"Adv Sci (Weinh)"},{"volume-title":"UTH_CCB System for Adverse Drug Reaction Extraction from Drug Labels at TAC-ADR 2017","year":"2017","author":"Xu","key":"2025112514464702100_ocaf113-B35"},{"key":"2025112514464702100_ocaf113-B36","first-page":"16","volume-title":"Proceedings of the 1st International Workshop on Medication and Adverse Drug Event Detection. vol. 90 of Proceedings of Machine Learning Research","author":"Chapman","year":"2018"},{"key":"2025112514464702100_ocaf113-B37","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1093\/jamia\/ocz101","article-title":"Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods","volume":"27","author":"Christopoulou","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"2025112514464702100_ocaf113-B38","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1093\/jamia\/ocz075","article-title":"An ensemble of neural models for nested adverse drug events and medication extraction with subwords","volume":"27","author":"Ju","year":"2020","journal-title":"J Am Med Inform Assoc"},{"key":"2025112514464702100_ocaf113-B39","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.jbi.2010.11.001","article-title":"Semi-automatic semantic annotation of PubMed queries: a study on quality, efficiency, satisfaction","volume":"44","author":"N\u00e9v\u00e9ol","year":"2011","journal-title":"J Biomed Inform"},{"key":"2025112514464702100_ocaf113-B40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/3763940","article-title":"Joint extraction of long-distance entity relation by aggregating local- and semantic-dependent features","volume":"2022","author":"Wei","year":"2022","journal-title":"Wireless Commun Mobile Comput"},{"key":"2025112514464702100_ocaf113-B41","doi-asserted-by":"crossref","first-page":"e17934","DOI":"10.2196\/17934","article-title":"hybrid deep learning for medication-related information extraction from clinical texts in French: MedExt Algorithm Development Study","volume":"9","author":"Jouffroy","year":"2021","journal-title":"JMIR Med Inform"},{"key":"2025112514464702100_ocaf113-B42","doi-asserted-by":"crossref","first-page":"407","DOI":"10.3390\/info15070407","article-title":"Multi-level attention with 2d table-filling for joint entity-relation extraction","volume":"15","author":"Zhang","year":"2024","journal-title":"Information"},{"key":"2025112514464702100_ocaf113-B43","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s40264-018-0762-z","article-title":"Overview of the first natural language processing challenge for extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0)","volume":"42","author":"Jagannatha","year":"2019","journal-title":"Drug Saf"},{"key":"2025112514464702100_ocaf113-B44","doi-asserted-by":"crossref","first-page":"104432","DOI":"10.1016\/j.jbi.2023.104432","article-title":"Overview of the 2022 n2c2 shared task on contextualized medication event extraction in clinical notes","volume":"144","author":"Mahajan","year":"2023","journal-title":"J Biomed Inform"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/32\/12\/1855\/64615134\/ocaf113.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/32\/12\/1855\/64615134\/ocaf113.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T19:46:59Z","timestamp":1764100019000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/32\/12\/1855\/8281727"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,11]]},"references-count":44,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,10,11]]},"published-print":{"date-parts":[[2025,12,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocaf113","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"type":"print","value":"1067-5027"},{"type":"electronic","value":"1527-974X"}],"subject":[],"published-other":{"date-parts":[[2025,12]]},"published":{"date-parts":[[2025,10,11]]}}}