Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data

OBJECTIVE: To advance use of real-world data (RWD) for pharmacovigilance, we sought to integrate a high-sensitivity natural language processing (NLP) pipeline for detecting potential adverse drug events (ADEs) with easily interpretable output for high-efficiency human review and adjudication of true...

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Detalles Bibliográficos
Autores principales: Geva, Alon, Stedman, Jason P, Manzi, Shannon F, Lin, Chen, Savova, Guergana K, Avillach, Paul, Mandl, Kenneth D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660953/
https://www.ncbi.nlm.nih.gov/pubmed/33215076
http://dx.doi.org/10.1093/jamiaopen/ooaa031

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660953/
https://www.ncbi.nlm.nih.gov/pubmed/33215076
http://dx.doi.org/10.1093/jamiaopen/ooaa031

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