GAN-Based Privacy-Preserving Intelligent Medical Consultation Decision-Making
Author:
Funder
Hubei Provincial Key Laboratory of Metallurgical Industry Process System Science
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10726-024-09902-z.pdf
Reference51 articles.
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2. Chamikara MAP, Bertok L, Camtepe K (2018) Efficient data perturbation for privacy preserving and accurate data stream mining. Pervasive Mob Comput 48:1–19. https://doi.org/10.1016/j.pmcj.2018.05.003
3. Che Z, Cheng Y, Zhai S, Sun Z, Liu Y (2017) Boosting deep learning risk prediction with generative adversarial networks for electronic health records. In: 2017 IEEE international conference on data mining (ICDM), pp 787–792. https://doi.org/10.1109/ICDM.2017.93
4. Douzas G, Bacao F (2018) Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst Appl 91:464–471. https://doi.org/10.1016/j.eswa.2017.09.030
5. Dove ES, Phillips M (2015) Privacy law, data sharing policies, and medical data: A comparative perspective. Springer, Berlin, pp 639–678. https://doi.org/10.1007/978-3-319-23633-9_24
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