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Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance
We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome—ast...
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| Publicado no: | PLoS One |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6638773/ https://ncbi.nlm.nih.gov/pubmed/31318885 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0210689 |
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