{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T18:45:42Z","timestamp":1700246742035},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>Recently, an active area of research in pharmacovigilance is to use social media such as Twitter as an alternative data source to gather patient-generated information pertaining to medication use. Most of thr published work focuses on identifying mentions of adverse effects in social media data but rarely investigating the relationship between a mentioned medication and any mentioned effect expressions. In this study, we treated this relation extraction task as a classification problem, and represented the Twitter text with neural embedding which was fed to a recurrent neural network classifier. The classification performance of our method was investigated in comparison with 4 baseline word embedding methods on a corpus of 9516 annotated tweets.<\/jats:p>","DOI":"10.3233\/shti220182","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:33:41Z","timestamp":1654594421000},"source":"Crossref","is-referenced-by-count":1,"title":["Extraction of Medication-Effect Relations in Twitter Data with Neural Embedding and Recurrent Neural Network"],"prefix":"10.3233","author":[{"given":"Keyuan","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Computer Information Technology & Graphics, Purdue University Northwest, Hammond, Indiana, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingkai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Intelligent Engineering, Ningbo City College of Vocational Technology, Ningbo, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gordon R.","family":"Bernard","sequence":"additional","affiliation":[{"name":"Department of Medicine, Vanderbilt University, Nashville, Tennessee, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220182","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:33:42Z","timestamp":1654594422000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220182","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}