Deep learning models in detection of dietary supplement adverse event signals from Twitter

OBJECTIVE: The objective of this study is to develop a deep learning pipeline to detect signals on dietary supplement-related adverse events (DS AEs) from Twitter. MATERIALS AND METHODS: We obtained 247 807 tweets ranging from 2012 to 2018 that mentioned both DS and AE. We designed a tailor-made ann...

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Detalles Bibliográficos
Autores principales: Wang, Yefeng, Zhao, Yunpeng, Schutte, Dalton, Bian, Jiang, Zhang, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497875/
https://www.ncbi.nlm.nih.gov/pubmed/34632323
http://dx.doi.org/10.1093/jamiaopen/ooab081

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8497875/
https://www.ncbi.nlm.nih.gov/pubmed/34632323
http://dx.doi.org/10.1093/jamiaopen/ooab081

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