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Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation

OBJECTIVE: 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 provide...

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Detalhes bibliográficos
Publicado no:J Am Med Inform Assoc
Main Authors: Xie, Jiaheng, Liu, Xiao, Dajun Zeng, Daniel
Formato: Artigo
Idioma:Inglês
Publicado em: Oxford University Press 2017
Assuntos:
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC6455898/
https://ncbi.nlm.nih.gov/pubmed/28505280
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/jamia/ocx045
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