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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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| Foilsithe in: | JAMIA Open |
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| Main Authors: | , , , , , , |
| Formáid: | Artigo |
| Teanga: | Inglês |
| Foilsithe: |
Oxford University Press
2020
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| Ábhair: | |
| Rochtain Ar Líne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7660953/ https://ncbi.nlm.nih.gov/pubmed/33215076 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/jamiaopen/ooaa031 |
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