{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T10:34:09Z","timestamp":1770201249662,"version":"3.49.0"},"reference-count":11,"publisher":"Georg Thieme Verlag KG","issue":"01\/02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Methods Inf Med"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2003The short time frame between the coronavirus disease 2019 (COVID-19) pandemic declaration and the vaccines authorization led to concerns among public regarding the safety and efficacy of the vaccines. The Food and Drug Administration uses the Vaccine Adverse Events Reporting System (VAERS) where general population can report their vaccine side effects in the text box. This information could be utilized to determine self-reported vaccine side effects.<\/jats:p><jats:p>\n          Objective\u2003To develop a supervised and unsupervised natural language processing (NLP) pipeline to extract self-reported COVID-19 vaccination side effects, location of the side effects, medications, and possibly false\/misinformation seeking further investigation in a structured format for analysis and reporting.<\/jats:p><jats:p>\n          Methods\u2003We utilized the VAERS dataset of COVID-19 vaccine reports from November 2020 to August 2022 of 725,246 individuals. We first developed a gold-standard (GS) dataset of randomly selected 1,500 records. Second, the GS was split into training, testing, and validation sets. The training dataset was used to develop the NLP applications (supervised and unsupervised) and testing and validation datasets were used to test the performances of the NLP application.<\/jats:p><jats:p>\n          Results\u2003The NLP application automatically extracted vaccine side effects, body locations of the side effects, medication, and possibly misinformation with moderate to high accuracy (84% sensitivity, 82% specificity, and 83% F-1 measure). We found that 23% people (386,270) faced arm soreness, 31% body swelling (226,208), 23% fatigue\/body weakness (168,160), and 22% (159,873) cold\/flue-like symptoms. Most of the complications occurred in the body locations such as the arm, back, chest, neck, face, and head. Over-the-counter pain medications such as Tylenol and Ibuprofen and allergy medication like Benadryl were most reported self-reported medications. Death due to COVID-19, changes in the DNA, and infertility were possible false\/misinformation reported by people.<\/jats:p><jats:p>\n          Conclusion\u2003Some self-reported side effects such as syncope, arthralgia, and blood clotting need further clinical investigations. Our NLP application may help in extracting information from big free-text electronic datasets to help policy makers and other researchers with decision making.<\/jats:p>","DOI":"10.1055\/s-0042-1760248","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:45:37Z","timestamp":1673311537000},"page":"049-059","source":"Crossref","is-referenced-by-count":3,"title":["Automatic Identification of Self-Reported COVID-19 Vaccine Information from Vaccine Adverse Events Reporting System"],"prefix":"10.1055","volume":"62","author":[{"given":"Jay S.","family":"Patel","sequence":"additional","affiliation":[{"name":"Health Services Administration and Policy, Temple University, College of Public Health, Philadelphia, Pennsylvania, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonya","family":"Zhan","sequence":"additional","affiliation":[{"name":"Health Services Administration and Policy, Temple University, College of Public Health, Philadelphia, Pennsylvania, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zasim","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Pharmaceutical Systems and Policy, West Virginia University, Morgantown, West Virginia, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bari","family":"Dzomba","sequence":"additional","affiliation":[{"name":"Health Services Administration and Policy, Temple University, College of Public Health, Philadelphia, Pennsylvania, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanmei","family":"Wu","sequence":"additional","affiliation":[{"name":"Health Services Administration and Policy, Temple University, College of Public Health, Philadelphia, Pennsylvania, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"issue":"05","key":"ref7","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1056\/NEJMoa2035389","article-title":"efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine","volume":"384","author":"L R Baden","year":"2021","journal-title":"N Engl J Med"},{"issue":"27","key":"ref8","doi-asserted-by":"crossref","first-page":"2603","DOI":"10.1056\/NEJMoa2034577","article-title":"Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine","volume":"383","author":"F P Polack","year":"2020","journal-title":"N Engl J Med"},{"issue":"23","key":"ref9","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1056\/NEJMoa2101544","article-title":"Safety and efficacy of single-dose Ad26.COV2.S vaccine against COVID-19","volume":"384","author":"J Sadoff","year":"2021","journal-title":"N Engl J Med"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.jbi.2016.07.023","article-title":"Decision support environment for medical product safety surveillance","volume":"64","author":"T Botsis","year":"2016","journal-title":"J Biomed Inform"},{"issue":"05","key":"ref15","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1136\/amiajnl-2010-000022","article-title":"Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection","volume":"18","author":"T Botsis","year":"2011","journal-title":"J Am Med Inform Assoc"},{"key":"ref19","volume-title":"A Prototype Tool Set to Support Machine-Assisted Annotation","author":"B R South","year":"2012"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"0520101","DOI":"10.1088\/1757-899X\/662\/5\/052010","article-title":"Convolution neural network for text mining and natural language processing","volume":"662","author":"N I Widiastuti","year":"2019","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"ref23","doi-asserted-by":"crossref","volume-title":"Review on natural language processing trends and techniques using NLTK","author":"D Yogish","DOI":"10.1007\/978-981-13-9187-3_53"},{"issue":"03","key":"ref24","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1197\/jamia.M1733","article-title":"Agreement, the f-measure, and reliability in information retrieval","volume":"12","author":"G Hripcsak","year":"2005","journal-title":"J Am Med Inform Assoc"},{"issue":"01","key":"ref26","doi-asserted-by":"crossref","first-page":"10946","DOI":"10.1038\/s41598-022-13809-7","article-title":"Comparability of clinical trials and spontaneous reporting data regarding COVID-19 vaccine safety","volume":"12","author":"C Luo","year":"2022","journal-title":"Sci Rep"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"n2211","DOI":"10.1136\/bmj.n2211","article-title":"Menstrual changes after covid-19 vaccination","volume":"374","author":"V Male","year":"2021","journal-title":"BMJ"}],"container-title":["Methods of Information in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/s-0042-1760248.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T02:01:33Z","timestamp":1683770493000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/s-0042-1760248"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,9]]},"references-count":11,"journal-issue":{"issue":"01\/02","published-online":{"date-parts":[[2023,5,9]]},"published-print":{"date-parts":[[2023,5]]}},"URL":"https:\/\/doi.org\/10.1055\/s-0042-1760248","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["0026-1270","2511-705X"],"issn-type":[{"value":"0026-1270","type":"print"},{"value":"2511-705X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,9]]}}}