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In this study, we propose a deep neural network (called LSTM\u2010CRF) combining long short\u2010term memory (LSTM) neural networks (a type of recurrent neural networks) and conditional random fields (CRFs) to recognize ADR mentions from social media in medicine and investigate the effects of three factors on ADR mention recognition. The three factors are as follows: (1) representation for continuous and discontinuous ADR mentions: two novel representations, that is, \u201cBIOHD\u201d and \u201cMultilabel,\u201d are compared; (2) subject of posts: each post has a subject (i.e., drug here); and (3) external knowledge bases. Experiments conducted on a benchmark corpus, that is, CADEC, show that LSTM\u2010CRF achieves better <jats:italic>F<\/jats:italic>\u2010score than CRF; \u201cMultilabel\u201d is better in representing continuous and discontinuous ADR mentions than \u201cBIOHD\u201d; both subjects of comments and external knowledge bases are individually beneficial to ADR mention recognition. To the best of our knowledge, this is the first time to investigate deep neural networks to mine continuous and discontinuous ADRs from social media.<\/jats:p>","DOI":"10.1155\/2018\/2379208","type":"journal-article","created":{"date-parts":[[2018,4,19]],"date-time":"2018-04-19T23:30:43Z","timestamp":1524180643000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM\u2010CRF"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0271-8246","authenticated-orcid":false,"given":"Buzhou","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianglu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingcai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,4,19]]},"reference":[{"key":"e_1_2_9_1_2","unstructured":"Online Support Groups and Forums at DailyStrength http:\/\/www.dailystrength.org."},{"key":"e_1_2_9_2_2","unstructured":"Ask a Patients http:\/\/www.askapatient.com."},{"key":"e_1_2_9_3_2","unstructured":"MedHelp Medical Support Communities http:\/\/www.medhelp.org\/forums\/list."},{"key":"e_1_2_9_4_2","unstructured":"PatientsLikeMe: live better together http:\/\/www.patientslikeme.com."},{"key":"e_1_2_9_5_2","first-page":"45","article-title":"Guideline for good clinical practice","volume":"47","author":"Guideline ICHHT","year":"2001","journal-title":"Journal of Postgraduate Medicine"},{"key":"e_1_2_9_6_2","unstructured":"Segura-BedmarI. 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