Mining multi-center heterogeneous medical data with distributed synthetic learning

Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the...

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Detalles Bibliográficos
Autores principales: Chang, Qi, Yan, Zhennan, Zhou, Mu, Qu, Hui, He, Xiaoxiao, Zhang, Han, Baskaran, Lohendran, Al’Aref, Subhi, Li, Hongsheng, Zhang, Shaoting, Metaxas, Dimitris N.
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/PMC10484909/
https://www.ncbi.nlm.nih.gov/pubmed/37679325
http://dx.doi.org/10.1038/s41467-023-40687-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484909/
https://www.ncbi.nlm.nih.gov/pubmed/37679325
http://dx.doi.org/10.1038/s41467-023-40687-y

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