Generative adversarial networks for imputing missing data for big data clinical research

BACKGROUND: Missing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data imputation approach, has the potential to substitute missing data accurately and efficiently but has not yet been evaluated in empirical big clinical dat...

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Detalles Bibliográficos
Autores principales: Dong, Weinan, Fong, Daniel Yee Tak, Yoon, Jin-sun, Wan, Eric Yuk Fai, Bedford, Laura Elizabeth, Tang, Eric Ho Man, Lam, Cindy Lo Kuen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059005/
https://www.ncbi.nlm.nih.gov/pubmed/33879090
http://dx.doi.org/10.1186/s12874-021-01272-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059005/
https://www.ncbi.nlm.nih.gov/pubmed/33879090
http://dx.doi.org/10.1186/s12874-021-01272-3

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