Quantifying representativeness in randomized clinical trials using machine learning fairness metrics

OBJECTIVE: We help identify subpopulations underrepresented in randomized clinical trials (RCTs) cohorts with respect to national, community-based or health system target populations by formulating population representativeness of RCTs as a machine learning (ML) fairness problem, deriving new repres...

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Detalles Bibliográficos
Autores principales: Qi, Miao, Cahan, Owen, Foreman, Morgan A, Gruen, Daniel M, Das, Amar K, Bennett, Kristin P
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/PMC8460438/
https://www.ncbi.nlm.nih.gov/pubmed/34568771
http://dx.doi.org/10.1093/jamiaopen/ooab077

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460438/
https://www.ncbi.nlm.nih.gov/pubmed/34568771
http://dx.doi.org/10.1093/jamiaopen/ooab077

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