Automating Access to Real-World Evidence

INTRODUCTION: Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this...

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Detalles Bibliográficos
Autores principales: Gauthier, Marie-Pier, Law, Jennifer H., Le, Lisa W., Li, Janice J.N., Zahir, Sajda, Nirmalakumar, Sharon, Sung, Mike, Pettengell, Christopher, Aviv, Steven, Chu, Ryan, Sacher, Adrian, Liu, Geoffrey, Bradbury, Penelope, Shepherd, Frances A., Leighl, Natasha B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201015/
https://www.ncbi.nlm.nih.gov/pubmed/35719866
http://dx.doi.org/10.1016/j.jtocrr.2022.100340

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201015/
https://www.ncbi.nlm.nih.gov/pubmed/35719866
http://dx.doi.org/10.1016/j.jtocrr.2022.100340

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