An adversarial training framework for mitigating algorithmic biases in clinical machine learning

Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases t...

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Detalles Bibliográficos
Autores principales: Yang, Jenny, Soltan, Andrew A. S., Eyre, David W., Yang, Yang, Clifton, David A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050816/
https://www.ncbi.nlm.nih.gov/pubmed/36991077
http://dx.doi.org/10.1038/s41746-023-00805-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050816/
https://www.ncbi.nlm.nih.gov/pubmed/36991077
http://dx.doi.org/10.1038/s41746-023-00805-y

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