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
Source PublicationJOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
2018
Volume25Issue:1Pages:72-80
SubtypeArticle
AbstractRecent 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.
KeywordE-cigarette Adverse Event Bi-lstm Recurrent Neural Network Word Embedding Deep Neural Network
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1093/jamia/ocx045
WOS KeywordNAMED ENTITY RECOGNITION ; ELECTRONIC CIGARETTES ; METAMAP ; IMPACT ; SMOKING ; TEXT
Indexed BySCI ; SSCI
Language英语
Funding OrganizationUS National Institutes of Health(1R01DA037378-01) ; National Science Foundation(IIS-1553109 ; IIS-1552860)
WOS Research AreaComputer Science ; Health Care Sciences & Services ; Information Science & Library Science ; Medical Informatics
WOS SubjectComputer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Health Care Sciences & Services ; Information Science & Library Science ; Medical Informatics
WOS IDWOS:000419605800012
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21935
Collection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
Affiliation1.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
Recommended Citation
GB/T 7714
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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