SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data

One of the impacts of the coronavirus disease 2019 (COVID-19) pandemic has been a push for researchers to better exploit synthetic data and accelerate the design, analysis, and modeling of clinical trials. The unprecedented clinical efforts caused by COVID-19’s emergence will certainly boost future...

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Detalles Bibliográficos
Autores principales: Khorchani, Takoua, Gadiya, Yojana, Witt, Gesa, Lanzillotta, Delia, Claussen, Carsten, Zaliani, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825316/
https://www.ncbi.nlm.nih.gov/pubmed/35156066
http://dx.doi.org/10.1016/j.patter.2022.100453

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825316/
https://www.ncbi.nlm.nih.gov/pubmed/35156066
http://dx.doi.org/10.1016/j.patter.2022.100453

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