[
[1] HERNANDEZ, M.: Synthetic data generation for tabular health records: A systematic review, Neurocomputing, No. 493 (2022) 28-45https://www.sciencedirect.com/science/article/pii/S0925231222004349
]Search in Google Scholar
[
[2] LASKO, T.: Spectral Anonymization of Data, IEEE Transactions on Knowledge and Data Engineering 22, No. 3 (2010) 437-446http://europepmc.org/article/MED/21373375
]Search in Google Scholar
[
[3] BAOWALY, M.: Synthesizing electronic health records using improved generative adversarial networks, Journal of the American Medical Informatics Association 26, No. 3 (2019) 228–241 https://academic.oup.com/jamia/article/26/3/228/5235390
]Search in Google Scholar
[
[4] EMAM, K., et al.: Optimizing the synthesis of clinical trial data using sequential trees, J Am Med Inform Assoc 28, No. 1 (2021) doi: 10.1093/jamia/ocaa249
]Open DOISearch in Google Scholar
[
[5] TUCKER, A.: Generating high-fidelity synthetic patient data for assessing machine learning healthcare software, npj Digital Medicine 3, No. 10.1038 (2020) https://www.researchgate.net/publication/346754138_Generating_high-fidelity_synthetic_patient_data_for_assessing_machine_learning_healthcare_software
]Search in Google Scholar
[
[6] ALQAHTANI, H., et al.: Applications of generative adversarial networks (gans): An updated review, Archives of Computational Methods in Engineering, No. 28.2 (2021) 525-552https://link.springer.com/article/10.1007/s11831-019-09388-y
]Search in Google Scholar
[
[7] BOUROU, S.: A review of tabular data synthesis using gans on an ids dataset, Information 12, No. 9 (2021) 375 https://www.mdpi.com/2078-2489/12/9/375/htm
]Search in Google Scholar
[
[8] CHOI, E.: Generating multi-label discrete patient records using generative adversarial networks, Machine learning for healthcare conference, No. (2017) 286-305 http://proceedings.mlr.press/v68/choi17a
]Search in Google Scholar
[
[9] XU, L.: Synthesizing tabular data using conditional GAN, Massachusetts Institute of Technology, No. (2020) https://dspace.mit.edu/handle/1721.1/128349
]Search in Google Scholar
[
[10] PARK, N.: Data synthesis based on generative adversarial networks, arXiv preprint 1806, No. 03384 (2018) https://arxiv.org/abs/1806.03384
]Search in Google Scholar
[
[11] MCLACHAN, S., et al.: Using the CareMap with Health Incidents Statistics for Generating the Realistic Synthetic Electronic Healthcare Record, IEEE International Conference on Healthcare Informatics 2016, No. (2016) 439-448
]Search in Google Scholar
[
[12] KANG, C.: Is Synthetic Dataset Reliable for Benchmarking Generalizable Person Re-Identification?, arXiv preprint 2209.05047, No. (2022) https://arxiv.org/pdf/2209.05047.pdf
]Search in Google Scholar
[
[13] BOUROU, S., et al.: A review of tabular data synthesis using gans on an ids dataset, Information 12.09, No. (2021) 375https://www.mdpi.com/2078-2489/12/9/375
]Search in Google Scholar