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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Detalles Bibliográficos
Autores principales: Edo-Osagie, Oduwa, Smith, Gillian, Lake, Iain, Edeghere, Obaghe, De La Iglesia, Beatriz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6638773/
https://www.ncbi.nlm.nih.gov/pubmed/31318885
http://dx.doi.org/10.1371/journal.pone.0210689

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6638773/
https://www.ncbi.nlm.nih.gov/pubmed/31318885
http://dx.doi.org/10.1371/journal.pone.0210689

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