Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation
Xie, Jiaheng1; Liu, Xiao2; Zeng, Daniel Dajun1
2018
发表期刊JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
卷号25期号:1页码:72-80
文章类型Article
摘要Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys. Social media provides a large data repository of consumers' e-cigarette feedback and experiences, which are useful for e-cigarette safety surveillance. However, it is difficult to automatically interpret the informal and nontechnical consumer vocabulary about e-cigarettes in social media. This issue hinders the use of social media content for e-cigarette safety surveillance. Recent developments in deep neural network methods have shown promise for named entity extraction from noisy text. Motivated by these observations, we aimed to design a deep neural network approach to extract e-cigarette safety information in social media.
关键词E-cigarette Adverse Event Bi-lstm Recurrent Neural Network Word Embedding Deep Neural Network
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1093/jamia/ocx045
关键词[WOS]NAMED ENTITY RECOGNITION ; ELECTRONIC CIGARETTES ; METAMAP ; IMPACT ; SMOKING ; TEXT
收录类别SCI ; SSCI
语种英语
项目资助者US National Institutes of Health(1R01DA037378-01) ; National Science Foundation(IIS-1553109 ; IIS-1552860)
WOS研究方向Computer Science ; Health Care Sciences & Services ; Information Science & Library Science ; Medical Informatics
WOS类目Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Health Care Sciences & Services ; Information Science & Library Science ; Medical Informatics
WOS记录号WOS:000419605800012
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21935
专题复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
作者单位1.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA
2.Univ Utah, Dept Operat & Informat Syst, Salt Lake City, UT USA
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
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Xie, Jiaheng,Liu, Xiao,Zeng, Daniel Dajun. Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation[J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION,2018,25(1):72-80.
APA Xie, Jiaheng,Liu, Xiao,&Zeng, Daniel Dajun.(2018).Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION,25(1),72-80.
MLA Xie, Jiaheng,et al."Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation".JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 25.1(2018):72-80.
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