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...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 25; no. 1; pp. 72 - 81 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2018
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| Acceso en línea: | Ver este registro en EBSCOhost |