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Principles of Synthesizing Medical Datasets Cover
Open Access
|Jan 2023

References

  1. [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
  2. [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
  3. [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
  4. [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
  5. [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
  6. [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
  7. [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
  8. [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
  9. [9] XU, L.: Synthesizing tabular data using conditional GAN, Massachusetts Institute of Technology, No. (2020) https://dspace.mit.edu/handle/1721.1/128349
  10. [10] PARK, N.: Data synthesis based on generative adversarial networks, arXiv preprint 1806, No. 03384 (2018) https://arxiv.org/abs/1806.03384
  11. [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
  12. [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
  13. [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
DOI: https://doi.org/10.2478/aei-2022-0019 | Journal eISSN: 1338-3957 | Journal ISSN: 1335-8243
Language: English
Page range: 25 - 29
Submitted on: Aug 3, 2022
Accepted on: Oct 21, 2022
Published on: Jan 24, 2023
Published by: Technical University of Košice
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Michal Kolárik, Lucia Gojdičová, Ján Paralič, published by Technical University of Košice
This work is licensed under the Creative Commons Attribution 4.0 License.