{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T10:18:56Z","timestamp":1792664336491,"version":"4.5.0"},"reference-count":52,"publisher":"Elsevier BV","issue":"7","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T00:00:00Z","timestamp":1783728000000},"content-version":"am","delay-in-days":375,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"publisher","award":["T15LM007079"],"award-info":[{"award-number":["T15LM007079"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"NIH National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35GM131905"],"award-info":[{"award-number":["R35GM131905"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"NIH National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["T32GM145440"],"award-info":[{"award-number":["T32GM145440"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Med"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.medj.2025.100642","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T22:05:42Z","timestamp":1743804342000},"page":"100642","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":23,"title":["OnSIDES database: Extracting adverse drug events from drug labels using natural language processing models"],"prefix":"10.1016","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1060-7065","authenticated-orcid":false,"given":"Yutaro","family":"Tanaka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsin Yi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Belloni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Undina","family":"Gisladottir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jenna","family":"Kefeli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Patterson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Apoorva","family":"Srinivasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Zietz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaurav","family":"Sirdeshmukh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Berkowitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathleen","family":"LaRow Brown","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas P.","family":"Tatonetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.medj.2025.100642_bib1","doi-asserted-by":"crossref","first-page":"481","DOI":"10.7861\/clinmedicine.16-5-481","article-title":"Adverse drug reactions","volume":"16","author":"Coleman","year":"2016","journal-title":"Clin. Med."},{"key":"10.1016\/j.medj.2025.100642_bib2","volume":"13","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.medj.2025.100642_bib3","volume":"7","year":"2012","journal-title":"PLoS One"},{"key":"10.1016\/j.medj.2025.100642_bib4","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1177\/1060028018765159","article-title":"Cost of prescription drug\u2013related morbidity and mortality","volume":"52","author":"Watanabe","year":"2018","journal-title":"Ann. Pharmacother."},{"key":"10.1016\/j.medj.2025.100642_bib5","volume":"15","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.medj.2025.100642_bib6","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s40263-018-0507-7","article-title":"Risk of intracranial hemorrhage associated with the use of antidepressants inhibiting serotonin reuptake: a systematic review","volume":"32","author":"Douros","year":"2018","journal-title":"CNS Drugs"},{"key":"10.1016\/j.medj.2025.100642_bib7","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.3003377","article-title":"Data-driven prediction of drug effects and interactions","volume":"4","author":"Tatonetti","year":"2012","journal-title":"Sci. Transl. Med."},{"key":"10.1016\/j.medj.2025.100642_bib8","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2016.26","article-title":"A curated and standardized adverse drug event resource to accelerate drug safety research","volume":"3","author":"Banda","year":"2016","journal-title":"Sci. Data"},{"key":"10.1016\/j.medj.2025.100642_bib9","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1021\/ci4000079","article-title":"Adverse drug events: database construction and in silico prediction","volume":"53","author":"Cheng","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.medj.2025.100642_bib10","doi-asserted-by":"crossref","first-page":"100108","DOI":"10.1016\/j.patter.2020.100108","article-title":"Using machine learning to identify adverse drug effects posing increased risk to women","volume":"1","author":"Chandak","year":"2020","journal-title":"Patterns"},{"key":"10.1016\/j.medj.2025.100642_bib11","first-page":"579","article-title":"A database of pediatric drug effects to evaluate ontogenic mechanisms from child growth and development","volume":"3","author":"Giangreco","year":"2022","journal-title":"M\u00e9dica Sur"},{"key":"10.1016\/j.medj.2025.100642_bib12","doi-asserted-by":"crossref","first-page":"887","DOI":"10.7326\/0003-4819-145-12-200612190-00144","article-title":"Literacy and misunderstanding prescription drug labels","volume":"145","author":"Davis","year":"2006","journal-title":"Ann. Intern. Med."},{"key":"10.1016\/j.medj.2025.100642_bib13","doi-asserted-by":"crossref","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The SIDER database of drugs and side effects","volume":"44","author":"Kuhn","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.medj.2025.100642_bib14","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv"},{"key":"10.1016\/j.medj.2025.100642_bib15","first-page":"1234","volume":"36","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.medj.2025.100642_bib16","article-title":"ClinicalBERT: modeling clinical notes and predicting hospital readmission","author":"Huang","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.medj.2025.100642_bib17","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1038\/s41746-021-00455-y","article-title":"Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction","volume":"4","author":"Rasmy","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.medj.2025.100642_bib18","unstructured":"Roberts, K., Demner-Fushman, D., and Tonning, J. 2017. Overview of the TAC 2017 Adverse Reaction Extraction from Drug Labels Track. Text Analysis Conference 2017. https:\/\/bionlp.nlm.nih.gov\/tac2017adversereactions\/."},{"key":"10.1016\/j.medj.2025.100642_bib19","doi-asserted-by":"crossref","first-page":"180001","DOI":"10.1038\/sdata.2018.1","article-title":"A dataset of 200 structured product labels annotated for adverse drug reactions","volume":"5","author":"Demner-Fushman","year":"2018","journal-title":"Sci. Data"},{"key":"10.1016\/j.medj.2025.100642_bib20","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.patrec.2020.12.013","article-title":"DeepCADRME: a deep neural model for complex adverse drug reaction mentions extraction","volume":"143","author":"El-allaly","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.medj.2025.100642_bib21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3195-5","article-title":"Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels","volume":"20","author":"Tiftikci","year":"2019","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.medj.2025.100642_bib22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3458754","article-title":"Domain-specific language model pretraining for biomedical natural language processing","volume":"3","author":"Gu","year":"2022","journal-title":"ACM Trans. Comput. Healthc."},{"key":"10.1016\/j.medj.2025.100642_bib23","article-title":"Revisiting few-sample BERT fine-tuning","author":"Zhang","year":"2021","journal-title":"arXiv"},{"key":"10.1016\/j.medj.2025.100642_bib24","series-title":"Clinical trials in paediatrics \u2014 regulatory and methodological aspects. Drug Discovery and Development - From Molecules to Medicine; Edited by Omboon Vallisuta and Suleiman Olimat","year":"2015"},{"key":"10.1016\/j.medj.2025.100642_bib25","author":"The Pharmaceutical Industry in Figures","year":"2022","journal-title":"European Federation of Pharmaceutical Industries and Associations"},{"key":"10.1016\/j.medj.2025.100642_bib26","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.jacbts.2016.06.003","article-title":"Drugs and devices","volume":"1","author":"Van Norman","year":"2016","journal-title":"JACC Basic Transl. Sci."},{"key":"10.1016\/j.medj.2025.100642_bib27","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s40264-015-0265-0","article-title":"Pediatric drug safety signal detection: a new drug\u2013event reference set for performance testing of data-mining methods and systems","volume":"38","author":"Osokogu","year":"2015","journal-title":"Drug Saf."},{"key":"10.1016\/j.medj.2025.100642_bib28","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1126\/science.1158140","article-title":"Drug target identification using side-effect similarity","volume":"321","author":"Campillos","year":"2008","journal-title":"Science"},{"key":"10.1016\/j.medj.2025.100642_bib29","volume":"6","year":"2011","journal-title":"PLoS One"},{"key":"10.1016\/j.medj.2025.100642_bib30","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s13321-020-0408-x","article-title":"Towards reproducible computational drug discovery","volume":"12","author":"Schaduangrat","year":"2020","journal-title":"J. Cheminf."},{"key":"10.1016\/j.medj.2025.100642_bib31","article-title":"ChemBERTa: large-scale self-supervised pretraining for molecular property prediction","author":"Chithrananda","year":"2020","journal-title":"arXiv"},{"key":"10.1016\/j.medj.2025.100642_bib32","article-title":"ChemBERTa-2: towards chemical foundation models","author":"Ahmad","year":"2022","journal-title":"arXiv"},{"key":"10.1016\/j.medj.2025.100642_bib33","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.medj.2025.100642_bib38","unstructured":"EMA (2019). European Medicines Agency (available at https:\/\/www.ema.europa.eu\/en)"},{"key":"10.1016\/j.medj.2025.100642_bib41","unstructured":"Electronic Medicines Compendium; 2021. https:\/\/www.medicines.org.uk\/emc."},{"key":"10.1016\/j.medj.2025.100642_bib42","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1002\/pro.3715","article-title":"Toward understanding the origin and evolution of cellular organisms","volume":"28","author":"Kanehisa","year":"2019","journal-title":"Protein Sci."},{"key":"10.1016\/j.medj.2025.100642_bib47","unstructured":"OHDSI. 2025. Athena. https:\/\/athena.ohdsi.org\/."},{"key":"10.1016\/j.medj.2025.100642_bib34","series-title":"Structured Product Labeling Resources, US Food and Drug Administration","year":"2023"},{"key":"10.1016\/j.medj.2025.100642_bib35","unstructured":"US Food and Drug Administration, Office of the Commissioner. 2009. Guidance for industry providing regulatory submissions in electronic format - drug establishment registration and drug listing. https:\/\/www.fda.gov\/media\/71146\/download."},{"key":"10.1016\/j.medj.2025.100642_bib36","unstructured":"US Food and Drug Administration, Office of the Commissioner. 2005. Guidance for industry providing regulatory submissions in electronic format - content of labeling electronic submissions. https:\/\/www.fda.gov\/media\/71129\/download."},{"key":"10.1016\/j.medj.2025.100642_bib37","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1093\/jamia\/ocab086","article-title":"Clinically relevant pretraining is all you need","volume":"28","author":"Bear Don\u2019t Walk IV","year":"2021","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"10.1016\/j.medj.2025.100642_bib39","author":"Medicines and Healthcare products Regulatory"},{"key":"10.1016\/j.medj.2025.100642_bib40","author":"Pharmaceuticals"},{"key":"10.1016\/j.medj.2025.100642_bib43","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.3390\/app11062663","article-title":"Automatic extraction of adverse drug reactions from summary of product characteristics","volume":"11","author":"Shen","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.medj.2025.100642_bib44","author":"Nakayama"},{"key":"10.1016\/j.medj.2025.100642_bib45","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"arXiv"},{"key":"10.1016\/j.medj.2025.100642_bib46","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1002\/pds.3408","article-title":"Safety information in drug labeling: a comparison of the USA, the UK, and Japan","volume":"22","author":"Shimazawa","year":"2013","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"10.1016\/j.medj.2025.100642_bib48","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: a major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.medj.2025.100642_bib49","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1136\/amiajnl-2012-001431","article-title":"Development and evaluation of an ensemble resource linking medications to their indications","volume":"20","author":"Wei","year":"2013","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"10.1016\/j.medj.2025.100642_bib50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-015-0069-3","article-title":"Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?","volume":"7","author":"Bajusz","year":"2015","journal-title":"J. Cheminf."},{"key":"10.1016\/j.medj.2025.100642_bib51","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: a benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci."},{"key":"10.1016\/j.medj.2025.100642_bib52","article-title":"RoBERTa: a robustly optimized BERT pretraining approach","author":"Liu","year":"2019","journal-title":"arXiv"}],"container-title":["Med"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666634025000698?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666634025000698?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T08:43:55Z","timestamp":1768034635000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666634025000698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":52,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["S2666634025000698"],"URL":"https:\/\/doi.org\/10.1016\/j.medj.2025.100642","relation":{},"ISSN":["2666-6340"],"issn-type":[{"value":"2666-6340","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"OnSIDES database: Extracting adverse drug events from drug labels using natural language processing models","name":"articletitle","label":"Article Title"},{"value":"Med","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.medj.2025.100642","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"100642"}}