Cargando…
Comment on: “Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts”
Autores principales: | Magge, Arjun, Sarker, Abeed, Nikfarjam, Azadeh, Gonzalez-Hernandez, Graciela |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515520/ https://www.ncbi.nlm.nih.gov/pubmed/31087070 http://dx.doi.org/10.1093/jamia/ocz013 |
Ejemplares similares
-
Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features
por: Nikfarjam, Azadeh, et al.
Publicado: (2015) -
Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts
por: Korkontzelos, Ioannis, et al.
Publicado: (2016) -
DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter
por: Magge, Arjun, et al.
Publicado: (2021) -
Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature
por: Magge, Arjun, et al.
Publicado: (2019) -
Adverse drug reactions reporting culture in Pharmacovigilance Programme of India
por: Kalaiselvan, Vivekanandan, et al.
Publicado: (2014)