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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...
Gespeichert in:
| Veröffentlicht in: | J Am Med Inform Assoc |
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| Hauptverfasser: | , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Oxford University Press
2017
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| Schlagworte: | |
| Online Zugang: | 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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