{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T01:42:09Z","timestamp":1744594929954},"reference-count":25,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T00:00:00Z","timestamp":1564617600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["clinicalkey.jp","clinicalkey.com","clinicalkey.es","clinicalkey.fr","clinicalkey.com.au","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Medical Informatics"],"published-print":{"date-parts":[[2019,8]]},"DOI":"10.1016\/j.ijmedinf.2019.04.017","type":"journal-article","created":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T06:30:53Z","timestamp":1558765853000},"page":"62-70","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":22,"special_numbering":"C","title":["Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer"],"prefix":"10.1016","volume":"128","author":[{"given":"Yixuan","family":"Tang","sequence":"first","affiliation":[]},{"given":"Jisong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Pei San","family":"Ang","sequence":"additional","affiliation":[]},{"given":"Sreemanee Raaj","family":"Dorajoo","sequence":"additional","affiliation":[]},{"given":"Belinda","family":"Foo","sequence":"additional","affiliation":[]},{"given":"Sally","family":"Soh","sequence":"additional","affiliation":[]},{"given":"Siew Har","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Mun Yee","family":"Tham","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Lynette","family":"Shek","sequence":"additional","affiliation":[]},{"given":"Cynthia","family":"Sung","sequence":"additional","affiliation":[]},{"given":"Anthony","family":"Tung","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ijmedinf.2019.04.017_bib0005","doi-asserted-by":"crossref","first-page":"19","DOI":"10.2165\/00002018-200932010-00002","article-title":"Determinants of under-reporting of adverse drug reactions","volume":"32","author":"Lopez-Gonzalez","year":"2009","journal-title":"Drug Saf."},{"issue":"5","key":"10.1016\/j.ijmedinf.2019.04.017_bib0010","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1136\/amiajnl-2013-001708","article-title":"Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text","volume":"20","author":"Eriksson","year":"2013","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"2","key":"10.1016\/j.ijmedinf.2019.04.017_bib0015","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1136\/amiajnl-2013-001612","article-title":"Mining clinical text for signals of adverse drug-drug interactions","volume":"21","author":"Iyer","year":"2013","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0020","first-page":"p1109","article-title":"Harnessing a health information exchange to identify surgical device adverse events for urogynecologic mesh","author":"Ballard","year":"2012","journal-title":"AMIA Annual Symposium proceedings, vol. 2012 American Medical Informatics Association"},{"issue":"2","key":"10.1016\/j.ijmedinf.2019.04.017_bib0025","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s40264-013-0132-9","article-title":"Signal detection of potentially drug-induced acute liver injury in children using a multi-country healthcare database network","volume":"37","author":"Ferrajolo","year":"2014","journal-title":"Drug Saf."},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0030","first-page":"682","article-title":"Using linked data for mining drug-drug interactions in electronic health records","volume":"192","author":"Pathak","year":"2013","journal-title":"Stud. Health Technol. Inform."},{"issue":"5","key":"10.1016\/j.ijmedinf.2019.04.017_bib0035","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1197\/jamia.M3167","article-title":"Computerized surveillance for adverse drug events in a pediatric hospital","volume":"16","author":"Kilbridge","year":"2009","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0040","first-page":"75","article-title":"Adverse-effect relations extraction from massive clinical records","author":"Miura","year":"2010","journal-title":"Proceedings of the Second Workshop on NLP Challenges in the Information Explosion Era (NLPIX 2010)"},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0045","first-page":"689","article-title":"Detecting adverse drug events in discharge summaries using variations on the simple bayes model","author":"Visweswaran","year":"2003","journal-title":"AMIA Annual Symposium Proceedings, vol. 2003 American Medical Informatics Association"},{"issue":"Supplement_1","key":"10.1016\/j.ijmedinf.2019.04.017_bib0050","doi-asserted-by":"crossref","first-page":"i144","DOI":"10.1136\/amiajnl-2011-000351","article-title":"Drug side effect extraction from clinical narratives of psychiatry and psychology patients","volume":"18","author":"Sohn","year":"2011","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"2","key":"10.1016\/j.ijmedinf.2019.04.017_bib0055","doi-asserted-by":"crossref","DOI":"10.2196\/publichealth.9361","article-title":"Clinical relation extraction toward drug safety surveillance using electronic health record narratives: classical learning versus deep learning","volume":"4","author":"Munkhdalai","year":"2018","journal-title":"JMIR Public Health Surveill."},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0060","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2018.2879744","article-title":"Exploring joint ab-lstm with embedded lemmas for adverse drug reaction discovery","author":"Santiso","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0065","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.jbi.2015.06.016","article-title":"On the creation of a clinical gold standard corpus in Spanish: mining adverse drug reactions","volume":"56","author":"Oronoz","year":"2015","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0070","doi-asserted-by":"crossref","DOI":"10.1007\/s40264-018-0766-8","article-title":"Towards drug safety surveillance and pharmacovigilance: current progress in detecting medication and adverse drug events from electronic health records","author":"Liu","year":"2019","journal-title":"Drug Saf."},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0075","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.bdr.2016.04.001","article-title":"Towards human-machine collaboration in creating an evaluation corpus for adverse drug events in discharge summaries of electronic medical records","volume":"4","author":"San Ang","year":"2016","journal-title":"Big Data Res."},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0080","series-title":"Mapping Text to the UMLS Metathesaurus","first-page":"1","author":"Aronson","year":"2006"},{"issue":"5","key":"10.1016\/j.ijmedinf.2019.04.017_bib0085","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1136\/jamia.2009.001560","article-title":"Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications","volume":"17","author":"Savova","year":"2010","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"5","key":"10.1016\/j.ijmedinf.2019.04.017_bib0090","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1006\/jbin.2001.1029","article-title":"A simple algorithm for identifying negated findings and diseases in discharge summaries","volume":"34","author":"Chapman","year":"2001","journal-title":"J. Biomed. Inform."},{"issue":"5","key":"10.1016\/j.ijmedinf.2019.04.017_bib0095","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1136\/jamia.2010.004200","article-title":"Community annotation experiment for ground truth generation for the i2b2 medication challenge","volume":"17","author":"Uzuner","year":"2010","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"4","key":"10.1016\/j.ijmedinf.2019.04.017_bib0100","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.jbi.2010.03.011","article-title":"Selecting information in electronic health records for knowledge acquisition","volume":"43","author":"Wang","year":"2010","journal-title":"J. Biomed. Inform."},{"issue":"4","key":"10.1016\/j.ijmedinf.2019.04.017_bib0105","first-page":"463","article-title":"Performance analysis of qos-based web service selection through integer programming","volume":"28","author":"Siadat","year":"2013","journal-title":"World Appl. Sci. J."},{"issue":"6","key":"10.1016\/j.ijmedinf.2019.04.017_bib0110","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1111\/bcp.13081","article-title":"Prevalence and characteristics of adverse drug reactions at admission to hospital: a prospective observational study","volume":"82","author":"Chan","year":"2016","journal-title":"Br. J. Clin. Pharmacol."},{"issue":"7","key":"10.1016\/j.ijmedinf.2019.04.017_bib0115","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1007\/s00520-014-2160-0","article-title":"Characteristics of unplanned hospital admissions due to drug-related problems in cancer patients","volume":"22","author":"Chan","year":"2014","journal-title":"Support. Care Cancer"},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0120","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1002\/phar.1896","article-title":"Identifying Potentially Avoidable Readmissions: A Medication-Based 15-Day Readmission Risk Stratification Algorithm","volume":"37","author":"Dorajoo","year":"2017","journal-title":"Pharmacotherapy"},{"key":"10.1016\/j.ijmedinf.2019.04.017_bib0125","doi-asserted-by":"crossref","DOI":"10.1038\/s41397-018-0053-1","article-title":"Economic burden of adverse drug reactions and potential for pharmacogenomic testing in Singaporean adults","author":"Chan","year":"2018","journal-title":"Pharmacogenomics J"}],"container-title":["International Journal of Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505618312504?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505618312504?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T02:15:28Z","timestamp":1559268928000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1386505618312504"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":25,"alternative-id":["S1386505618312504"],"URL":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2019.04.017","relation":{},"ISSN":["1386-5056"],"issn-type":[{"value":"1386-5056","type":"print"}],"subject":[],"published":{"date-parts":[[2019,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer","name":"articletitle","label":"Article Title"},{"value":"International Journal of Medical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2019.04.017","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}