CoChat
Sign Up
Cited by 95Machine Learning in HealthcareArtificial Intelligence in Healthcare and EducationPrivacy-Preserving Technologies in Data

A Survey of Generative Adversarial Networks for Synthesizing Structured Electronic Health Records

Open access
Ghosheh, Ghadeer O., Li, Jin, Zhu, Tingting·2024·ACM Computing Surveys
DOI 10.1145/3636424
Open this paper in CoChat — free accountOpen PDF

Abstract

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly Generative Adversarial Networks (GANs), show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to survey the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3636424

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 165

  1. Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. In Proceedings of the ACM SIGSAC Conference on Computer and Communica- tions Security. 308-318.
  2. Douglas Almond, Kenneth Y. Chay, and David S. Lee. 2005. The costs of low birth weight. Quart. J. Econ. 120, 3 (2005), 1031-1083.
  3. Martin Arjovsky and Léon Bottou. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862. Retrieved from https://arxiv.org/abs/1701.04862 · arXiv
  4. Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein generative adversarial networks. In Proceed- ings of the International Conference on Machine Learning. PMLR, 214-223.
  5. Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang. 2017. Generalization and equilibrium in generative adversarial nets (gans). In Proceedings of the International Conference on Machine Learning. PMLR, 224-232.
  6. Gilbert Badaro, Mohammed Saeed, and Paolo Papotti. 2023. Transformers for tabular data representation: A survey of models and applications. In Transactions of the Association for Computational Linguistics. MIT Press, Cambridge, MA.
  7. Mrinal Kanti Baowaly, Chia-Ching Lin, Chao-Lin Liu, and Kuan-Ta Chen. 2019. Synthesizing electronic health records using improved generative adversarial networks. J. Am. Med. Inf. Assoc. 26, 3 (2019), 228-241.
  8. Brett K. Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P. Bhavnani, James Brian Byrd, and Casey S. Greene. 2019. Privacy-preserving generative deep neural networks support clinical data sharing. Circulation: Cardiovasc. Qual. Outcomes 12, 7 (2019), e005122.
  9. Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, and Rémi Munos. 2017. The cramer distance as a solution to biased wasserstein gradients. arXiv:1705.10743. Retrieved from https://arxiv.org/abs/1705.10743 · arXiv
  10. Dmitry I. Belov and Ronald D. Armstrong. 2011. Distributions of the Kullback-Leibler divergence with applications. Br. J. Math. Statist. Psychol. 64, 2 (2011), 291-309.
  11. Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009. Pearson correlation coefficient. In Noise Re- duction in Speech Processing. Springer, 1-4.
  12. Alain Berlinet and Christine Thomas-Agnan. 2011. Reproducing Kernel Hilbert Spaces in Probability and Statistics. Springer Science & Business Media.
  13. Nick Black. 1996. Why we need observational studies to evaluate the effectiveness of health care. Br. Med. J. 312, 7040 (1996), 1215-1218.
  14. Jeanne Brooks-Gunn, Fong-ruey Liaw, and Pamela Kato Klebanov. 1992. Effects of early intervention on cognitive function of low birth weight preterm infants. J. Pediatr. 120, 3 (1992), 350-359.
  15. Eoin Brophy, Zhengwei Wang, Qi She, and Tomás Ward. 2023. Generative Adversarial Networks in Time Series: A Systematic Literature Review. Comput. Surv. 55, 10 (2023), 1-31.
  16. Dorothy Bulas and Alexia Egloff. 2013. Benefits and risks of MRI in pregnancy. In Seminars in Perinatology, Vol. 37. Elsevier, 301-304.
  17. Ramiro Camino, Christian Hammerschmidt, and Radu State. 2018. Generating multi-categorical samples with gen- erative adversarial networks. arXiv:1807.01202. Retrieved from https://arxiv.org/abs/1807.01202 · arXiv
  18. Ramiro D. Camino, Christian A. Hammerschmidt, and Radu State. 2019. Improving missing data imputation with deep generative models. arXiv:1902.10666. Retrieved from https://arxiv.org/abs/1902.10666 · arXiv
  19. Taha Ceritli, Ghadeer O. Ghosheh, Vinod Kumar Chauhan, Tingting Zhu, Andrew P. Creagh, and David A. Clifton. 2023. Synthesizing mixed-type electronic health records using diffusion models. arXiv:2302.14679. Retrieved from https://arxiv.org/abs/2302.14679 · arXiv
  20. Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner, Anton Becker, Olivio Donati, and Ender Konukoglu. 2019. Semi-supervised and task-driven data augmentation. In International Conference on Information Processing in Medical Imaging. Springer, 29-41.
  21. Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle, and Balaraman Ravindran. 2016. Correlational neural networks. Neural Comput. 28, 2 (2016), 257-285.
  22. Zhengping Che, Yu Cheng, Shuangfei Zhai, Zhaonan Sun, and Yan Liu. 2017. Boosting deep learning risk prediction with generative adversarial networks for electronic health records. In Proceedings of the IEEE International Conference on Data Mining (ICDM '17). IEEE, 787-792.
  23. Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Proceedings of the 30th Interna- tional Conference on Neural Information Processing Systems. 2180-2188.
  24. Zhaoyi Chen, Xiong Liu, William Hogan, Elizabeth Shenkman, and Jiang Bian. 2021. Applications of artificial intel- ligence in drug development using real-world data. Drug Discov. Today 26, 5 (2021), 1256-1264.
  25. Kieran Chin-Cheong, Thomas Sutter, and Julia E. Vogt. 2019. Generation of heterogeneous synthetic electronic health records using GANs. In Workshop on Machine Learning for health (ML4H) at the 33rd Conference on Neural Information Processing Systems (NeurIPS '19). ETH Zurich, Institute for Machine Learning.
  26. Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078. Retrieved from https://arxiv.org/abs/1406.1078 · arXiv
  27. Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, Michael Thompson, James Bost, Javier Tejedor-Sojo, and Jimeng Sun. 2016. Multi-layer representation learning for medical concepts. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1495-1504.
  28. Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, and Jimeng Sun. 2017. Generating multi- label discrete patient records using generative adversarial networks. In Machine Learning for Healthcare Conference. PMLR, 286-305.
  29. Edward Choi, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. 2016. Medical concept representation learning from electronic health records and its application on heart failure prediction. arXiv:1602.03686. Retrieved from https: //arxiv.org/abs/1602.03686 · arXiv
  30. Jiebin Chu, Wei Dong, Jinliang Wang, Kunlun He, and Zhengxing Huang. 2020. Treatment effect prediction with adversarial deep learning using electronic health records. BMC Med. Inf. Decis. Mak. 20, 4 (2020), 1-14.
  31. Jennifer A. Cleland, Keiko Abe, and Jan-Joost Rethans. 2009. The use of simulated patients in medical education: AMEE Guide No 42. Med. Teacher 31, 6 (2009), 477-486.
  32. Mike Conway, Richard L. Berg, David Carrell, Joshua C. Denny, Abel N. Kho, Iftikhar J. Kullo, James G. Linneman, Jennifer A. Pacheco, Peggy Peissig, Luke Rasmussen, et al. 2011. Analyzing the heterogeneity and complexity of Elec- tronic Health Record oriented phenotyping algorithms. In AMIA Annual Symposium Proceedings, Vol. 2011. American Medical Informatics Association, 274.
  33. Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A. Bharath. 2018. Generative adversarial networks: An overview. IEEE Sign. Process. Mag. 35, 1 (2018), 53-65.
  34. Arianna Dagliati, Alberto Malovini, Valentina Tibollo, and Riccardo Bellazzi. 2021. Health informatics and EHR to support clinical research in the COVID-19 pandemic: An overview. Brief. Bioinf. 22, 2 (2021), 812-822.
  35. Ralph B. D'Agostino Jr. 1998. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med. 17, 19 (1998), 2265-2281.
  36. Zongyu Dai, Zhiqi Bu, and Qi Long. 2021. Multiple imputation via generative adversarial network for high- dimensional blockwise missing value problems. In Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA '21). IEEE, 791-798.
  37. Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, and Ruslan Salakhutdinov. 2017. Good semi-supervised learn- ing that requires a bad gan. arXiv:1705.09783. Retrieved from https://arxiv.org/abs/1705.09783 · arXiv
  38. Saloni Dash, Andrew Yale, Isabelle Guyon, and Kristin P. Bennett. 2020. Medical time-series data generation using generative adversarial networks. In Artificial Intelligence in Medicine: 18th International Conference on Artificial Intel- ligence in Medicine (AIME '20). Springer, 382-391.
  39. Chloe de Grood, Jeanna Parsons Leigh, Sean M. Bagshaw, Peter M. Dodek, Robert A. Fowler, Alan J. Forster, Jamie M. Boyd, and Henry T. Stelfox. 2018. Patient, family and provider experiences with transfers from intensive care unit to hospital ward: A multicentre qualitative study. Can. Med. Assoc. J. 190, 22 (2018), E669-E676.
  40. Josep Domingo-Ferrer, David Sánchez, and Alberto Blanco-Justicia. 2021. The limits of differential privacy (and its misuse in data release and machine learning). Commun. ACM 64, 7 (2021), 33-35.
  41. Christopher J. Duff, Ivonne Solis-Trapala, Owen J. Driskell, David Holland, Helen Wright, Jenna L. Waldron, Clare Ford, Jonathan J. Scargill, Martin Tran, Fahmy WF Hanna, et al. 2019. The frequency of testing for glycated haemoglobin, HbA1c, is linked to the probability of achieving target levels in patients with suboptimally controlled diabetes mellitus. Clin. Chem. Labor. Med. 57, 2 (2019), 296-304.
  42. Cynthia Dwork, Aaron Roth, et al. 2014. The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9, 3-4 (2014), 211-407.
  43. Khaled El Emam, Lucy Mosquera, Xi Fang, and Alaa El-Hussuna. 2022. Utility metrics for evaluating synthetic health data generation methods: Validation study. JMIR Med. Inf. 10, 4 (2022), e35734.
  44. Cristóbal Esteban, Stephanie L. Hyland, and Gunnar Rätsch. 2017. Real-valued (medical) time series generation with recurrent conditional gans. arXiv:1706.02633. Retrieved from https://arxiv.org/abs/1706.02633 · arXiv
  45. Andre Esteva, Katherine Chou, Serena Yeung, Nikhil Naik, Ali Madani, Ali Mottaghi, Yun Liu, Eric Topol, Jeff Dean, and Richard Socher. 2021. Deep learning-enabled medical computer vision. NPJ Digit. Med. 4, 1 (2021), 1-9.
  46. Renee Fekieta, Alana Rosenberg, Beth Hodshon, Shelli Feder, Sarwat I. Chaudhry, and Beth L. Emerson. 2021. Or- ganisational factors underpinning intra-hospital transfers: A guide for evaluating context in quality improvement. Health Syst. 10, 4 (2021), 239-248.
  47. Tanner Fiez, Benjamin Chasnov, and Lillian J. Ratliff. 2019. Convergence of learning dynamics in stackelberg games. arXiv:1906.01217. Retrieved from https://arxiv.org/abs/1906.01217 · arXiv
  48. Centers for Disease Control, Prevention, et al. 2003. HIPAA privacy rule and public health. Guidance from CDC and the US Department of Health and Human Services. Morbid. Mortal. Week. Rep. 52, Suppl 1 (2003), 1-17.
  49. Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015. Model inversion attacks that exploit confidence informa- tion and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 1322-1333.
  50. Jeremy Georges-Filteau and Elisa Cirillo. 2020. Synthetic observational health data with GANs: From slow adoption to a boom in medical research and ultimately digital twins? arXiv:2005.13510. Retrieved from https://arxiv.org/abs/ 2005.13510 · arXiv
  51. Shantanu Ghosh, Christina Boucher, Jiang Bian, and Mattia Prosperi. 2021. Propensity score synthetic augmentation matching using generative adversarial networks (PSSAM-GAN). Comput. Methods Progr. Biomed. Update 1 (2021), 100020.
  52. Cory E. Goldstein, Charles Weijer, Jamie C. Brehaut, Dean A. Fergusson, Jeremy M. Grimshaw, Austin R. Horn, and Monica Taljaard. 2018. Ethical issues in pragmatic randomized controlled trials: A review of the recent literature identifies gaps in ethical argumentation. BMC Med. Ethics 19, 1 (2018), 1-10.
  53. Andre Goncalves, Priyadip Ray, Braden Soper, Jennifer Stevens, Linda Coyle, and Ana Paula Sales. 2020. Generation and evaluation of synthetic patient data. BMC Med. Res. Methodol. 20, 1 (2020), 1-40.
  54. Ian Goodfellow. 2016. Nips 2016 tutorial: Generative adversarial networks. arXiv:1701.00160. Retrieved from https: //arxiv.org/abs/1701.00160 · arXiv
  55. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola. 2012. A kernel two-sample test. J. Mach. Learn. Res. 13, 1 (2012), 723-773.
  56. SPRINT Research Group. 2015. A randomized trial of intensive versus standard blood-pressure control. New Engl. J. Med. 373, 22 (2015), 2103-2116.
  57. Mehak Gupta, Thao-Ly T Phan, H Timothy Bunnell, and Rahmatollah Beheshti. 2021. Concurrent imputation and prediction on EHR data using Bi-Directional GANs: Bi-GANs for EHR imputation and prediction. In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. 1-9.
  58. Mehak Gupta, Thao-Ly T Phan, Timothy Bunnell, and Rahmatollah Beheshti. 2019. Obesity prediction with EHR data: A deep learning approach with interpretable elements. arXiv:1912.02655. Retrieved from https://arxiv.org/abs/ 1912.02655 · arXiv
  59. RJ Hayes and S Bennett. 1999. Simple sample size calculation for cluster-randomized trials. Int. J. Epidemiol. 28, 2 (1999), 319-326.
  60. Catherine Helmer, Karine Pérès, Antoine Pariente, Florence Pasquier, Sophie Auriacombe, Michel Poncet, Florence Portet, Olivier Rouaud, Karen Ritchie, Christophe Tzourio, et al. 2008. Primary and secondary care consultations in elderly demented individuals in France. Dementia Geriatr. Cogn. Disord. 26, 5 (2008), 407-415.
  61. J. Henry, Yuriy Pylypchuk, Talisha Searcy, and Vaishali Patel. 2016. Adoption of electronic health record systems among US non-federal acute care hospitals: 2008-2015. ONC Data Brief 35 (2016), 1-9.
  62. Jennifer L. Hill. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20, 1 (2011), 217-240.
  63. R. Devon Hjelm, Athul Paul Jacob, Tong Che, Adam Trischler, Kyunghyun Cho, and Yoshua Bengio. 2017. Boundary- seeking generative adversarial networks. arXiv:1702.08431. Retrieved from https://arxiv.org/abs/1702.08431 · arXiv
  64. Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33 (2020), 6840-6851.
  65. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735-1780.
  66. Mohammad-Parsa Hosseini, Amin Hosseini, and Kiarash Ahi. 2020. A review on machine learning for EEG signal processing in bioengineering. IEEE Rev. Biomed. Eng. 14 (2020), 204-218.
  67. Mohammad Hossin and Md Nasir Sulaiman. 2015. A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manage. Process 5, 2 (2015), 1.
  68. Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. 2017. Globally and locally consistent image completion. ACM Trans. Graph. 36, 4 (2017), 1-14.
  69. Michael Imhoff. 1992. Acquisition of ICU data: Concepts and demands. Int. J. Clin. Monitor. Comput. 9, 4 (1992), 229-237.
  70. Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical reparameterization with gumbel-softmax. arXiv:1611.01144. · arXiv
  71. Nathalie Japkowicz and Shaju Stephen. 2002. The class imbalance problem: A systematic study. Intell. Data Anal. 6, 5 (2002), 429-449.
  72. Ahmedin Jemal, Andrea Thomas, Taylor Murray, Michael Thun, et al. 2002. Cancer statistics, 2002. Ca Cancer J. Clin. 52, 1 (2002), 23-47.
  73. Jiwoong J. Jeong, Amara Tariq, Tobiloba Adejumo, Hari Trivedi, Judy W. Gichoya, and Imon Banerjee. 2022. System- atic review of generative adversarial networks (GANs) for medical image classification and segmentation. J. Digit. Imag. (2022), 1-16.
  74. Alistair E. W. Johnson, Tom J. Pollard, Lu Shen, H Lehman Li-Wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark. 2016. MIMIC-III, a freely accessible critical care database. Sci. Data 3, 1 (2016), 1-9.
  75. James Jordon, Alan Wilson, and Mihaela van der Schaar. 2020. Synthetic data: Opening the data floodgates to enable faster, more directed development of machine learning methods. arXiv:2012.04580. Retrieved from https://arxiv.org/ abs/2012.04580 · arXiv
  76. James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar. 2018. PATE-GAN: Generating synthetic data with differ- ential privacy guarantees. In International Conference on Learning Representations.
  77. Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017. Progressive growing of gans for improved quality, stability, and variation. arXiv:1710.10196. Retrieved from https://arxiv.org/abs/1710.10196 · arXiv
  78. Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. 2022. Transformers in vision: A survey. ACM Comput. Surv. 54, 10s (2022), 1-41.
  79. Ellen Kim, Samuel M. Rubinstein, Kevin T. Nead, Andrzej P. Wojcieszynski, Peter E. Gabriel, and Jeremy L. Warner. 2019. The evolving use of electronic health records (EHR) for research. In Seminars in Radiation Oncology, Vol. 29. Elsevier, 354-361.
  80. Joanne Kim, Gilad Horowitz, Michael Hong, Mario Orsini, Sylvia L. Asa, and Kevin Higgins. 2017. The dangers of parathyroid biopsy. J. Otolaryngol. Head Neck Surg. 46, 1 (2017), 1-4.
  81. Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv:1312.6114. Retrieved from https: //arxiv.org/abs/1312.6114 · arXiv
  82. Virginia Klema and Alan Laub. 1980. The singular value decomposition: Its computation and some applications. IEEE Trans. Automat. Contr. 25, 2 (1980), 164-176.
  83. Andrew W. Knight and Timothy P. Senior. 2006. The common problem of rare disease in general practice. Med. J. Austr. 185, 2 (2006), 82-83.
  84. John Krumm. 2018. Ubiquitous Computing Fundamentals. CRC Press.
  85. The Lancet. 2018. Personalised medicine in the UK. Lancet (London, Engl.) 391, 10115 (2018), e1.
  86. Dongha Lee, Hwanjo Yu, Xiaoqian Jiang, Deevakar Rogith, Meghana Gudala, Mubeen Tejani, Qiuchen Zhang, and Li Xiong. 2020. Generating sequential electronic health records using dual adversarial autoencoder. J. Am. Med. Inf. Assoc. 27, 9 (2020), 1411-1419.
  87. Jin Li, Benjamin J. Cairns, Jingsong Li, and Tingting Zhu. 2021. Generating synthetic mixed-type longitudinal elec- tronic health records for artificial intelligent applications. arXiv:2112.12047. Retrieved from https://arxiv.org/abs/ 2112.12047 · arXiv
  88. Wenyuan Li, Yunlong Wang, Yong Cai, Corey Arnold, Emily Zhao, and Yilian Yuan. 2018. Semi-supervised rare disease detection using generative adversarial network. arXiv:1812.00547. Retrieved from https://arxiv.org/abs/1812. 00547 · arXiv
  89. Yunzhe Li, Kun Kuang, Bo Li, Peng Cui, Jianrong Tao, Hongxia Yang, and Fei Wu. 2020. Continuous treatment effect estimation via generative adversarial de-confounding. In Proceedings of the KDD Workshop on Causal Discovery. PMLR, 4-22.
  90. Roderick J. A. Little and Donald B. Rubin. 2019. Statistical Analysis with Missing Data. Vol. 793. John Wiley & Sons.
  91. Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, et al. 2018. Multivariate time series imputation with generative adversarial networks. Adv. Neural Inf. Process. Syst. 31 (2018).
  92. Yonghong Luo, Ying Zhang, Xiangrui Cai, and Xiaojie Yuan. 2019. E2gan: End-to-end generative adversarial net- work for multivariate time series imputation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. AAAI Press, 3094-3100.
  93. Jeanne M. Madden, Matthew D. Lakoma, Donna Rusinak, Christine Y. Lu, and Stephen B. Soumerai. 2016. Missing clinical and behavioral health data in a large electronic health record (EHR) system. J. Am. Med. Inf. Assoc. 23, 6 (2016), 1143-1149.
  94. A. P. Majtey, P. W. Lamberti, and D. P. Prato. 2005. Jensen-Shannon divergence as a measure of distinguishability between mixed quantum states. Phys. Rev. A 72, 5 (2005), 052310.
  95. Tatiana Malygina, Elena Ericheva, and Ivan Drokin. 2019. Data augmentation with GAN: Improving chest X-Ray pathologies prediction on class-imbalanced cases. In International Conference on Analysis of Images, Social Networks and Texts. Springer, 321-334.
  96. Navin Kumar Manaswi. 2018. Rnn and lstm. In Deep Learning with Applications Using Python. Springer, 115-126.
  97. Benjamin Marlin. 2008. Missing Data Problems in Machine Learning. Ph. D. Dissertation.
  98. Stan Matwin, Jordi Nin, Morvarid Sehatkar, and Tomasz Szapiro. 2015. A review of attribute disclosure control. Adv. Res. Data Priv. (2015), 41-61.
  99. Matthew McDermott, Tom Yan, Tristan Naumann, Nathan Hunt, Harini Suresh, Peter Szolovits, and Marzyeh Ghas- semi. 2018. Semi-supervised biomedical translation with cycle wasserstein regression GANs. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.
  100. Roseanne McNamee. 2003. Confounding and confounders. Occupat. Environ. Med. 60, 3 (2003), 227-234.
  101. Bradley D. Menz, Sophie L. Stocker, Nick Verougstraete, Danijela Kocic, Peter Galettis, Christophe P. Stove, and Stephanie E. Reuter. 2021. Barriers and opportunities for the clinical implementation of therapeutic drug monitoring in oncology. Br. J. Clin. Pharmacol. 87, 2 (2021), 227-236.
  102. Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein. 2016. Unrolled generative adversarial networks. arXiv:1611.02163. Retrieved from https://arxiv.org/abs/1611.02163 · arXiv
  103. Lu Mi, Macheng Shen, and Jingzhao Zhang. 2018. A probe towards understanding gan and vae models. arXiv:1812.05676. Retrieved from https://arxiv.org/abs/1812.05676 · arXiv
  104. Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv:1411.1784. Retrieved from https://arxiv.org/abs/1411.1784 · arXiv
  105. George B. Moody, Roger G. Mark, and Ary L. Goldberger. 2001. PhysioNet: A web-based resource for the study of physiologic signals. IEEE Eng. Med. Biol. Mag. 20, 3 (2001), 70-75.
  106. Simon J. Newsome, Ruth H. Keogh, and Rhian M. Daniel. 2018. Estimating long-term treatment effects in observa- tional data: A comparison of the performance of different methods under real-world uncertainty. Stat. Med. 37, 15 (2018), 2367-2390.
  107. Frank Nielsen. 2020. On a generalization of the Jensen-Shannon divergence and the Jensen-Shannon centroid. En- tropy 22, 2 (2020), 221.
  108. Augustus Odena. 2016. Semi-supervised learning with generative adversarial networks. arXiv:1606.01583. Retrieved from https://arxiv.org/abs/1606.01583 · arXiv
  109. Nicolas Papernot, Martn Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar. 2016. Semi-supervised knowl- edge transfer for deep learning from private training data. arXiv:1610.05755. Retrieved from https://arxiv.org/abs/ 1610.05755 · arXiv
  110. Tom J. Pollard, Alistair E. W. Johnson, Jesse D. Raffa, Leo A. Celi, Roger G. Mark, and Omar Badawi. 2018. The eICU collaborative research database, a freely available multi-center database for critical care research. Sci. Data 5, 1 (2018), 1-13.
  111. Yeping Lina Qiu, Hong Zheng, and Olivier Gevaert. 2020. Genomic data imputation with variational auto-encoders. GigaScience 9, 8 (2020).
  112. Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434. Retrieved from https://arxiv.org/abs/1511.06434 · arXiv
  113. Sina Rashidian, Fusheng Wang, Richard Moffitt, Victor Garcia, Anurag Dutt, Wei Chang, Vishwam Pandya, Janos Hajagos, Mary Saltz, and Joel Saltz. 2020. SMOOTH-GAN: Towards sharp and smooth synthetic EHR data generation. In Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine (AIME '20). Springer, 37-48.
  114. Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. 2019. Generating diverse high-fidelity images with vq-vae-2. Advances in Neural Information Processing Systems 32 (2019).
  115. Paul R. Rosenbaum. 2002. Covariance adjustment in randomized experiments and observational studies. Statist. Sci. 17, 3 (2002), 286-327.
  116. Halley Ruppel, Aashish Bhardwaj, Raj N. Manickam, Julia Adler-Milstein, Marc Flagg, Manuel Ballesca, and Vin- cent X. Liu. 2020. Assessment of electronic health record search patterns and practices by practitioners in a large integrated health care system. JAMA Netw. Open 3, 3 (2020), e200512-e200512.
  117. Ludger Rüschendorf. 1985. The Wasserstein distance and approximation theorems. Probab. Theory Relat. Fields 70, 1 (1985), 117-129.
  118. Pegah Salehi, Abdolah Chalechale, and Maryam Taghizadeh. 2020. Generative adversarial networks (GANs): An overview of theoretical model, evaluation metrics, and recent developments. arXiv:2005.13178. Retrieved from https: //arxiv.org/abs/2005.13178 · arXiv
  119. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved tech- niques for training gans. Adv. Neural Inf. Process. Syst. 29 (2016), 2234-2242.
  120. Sara Santiso, Arantza Casillas, and Alicia Pérez. 2019. The class imbalance problem detecting adverse drug reactions in electronic health records. Health Inf. J. 25, 4 (2019), 1768-1778.
  121. Ashish Sarraju, Andrew Ward, Sukyung Chung, Jiang Li, David Scheinker, and Fàtima Rodriguez. 2021. Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic pa- tients. Open Heart 8, 2 (2021), e001802.
  122. Uri Shalit, Fredrik D. Johansson, and David Sontag. 2017. Estimating individual treatment effect: Generalization bounds and algorithms. In International Conference on Machine Learning. PMLR, 3076-3085.
  123. Farah Shamout, Tingting Zhu, and David A. Clifton. 2020. Machine learning for clinical outcome prediction. IEEE Rev. Biomed. Eng. 14 (2020), 116-126.
  124. A. Mi Shin, In Hee Lee, Gyeong Ho Lee, Hee Joon Park, Hyung Seop Park, Kyung Il Yoon, Jung Jeung Lee, and Yoon Nyun Kim. 2010. Diagnostic analysis of patients with essential hypertension using association rule mining. Healthcare Inf. Res. 16, 2 (2010), 77-81.
  125. Leif Sörnmo and Pablo Laguna. 2006. Electrocardiogram (ECG) signal processing. In Wiley Encyclopedia of Biomedical Engineering.
  126. Chenxi Sun, Shenda Hong, Moxian Song, and Hongyan Li. 2020. A review of deep learning methods for irregularly sampled medical time series data. arXiv:2010.12493. Retrieved from https://arxiv.org/abs/2010.12493 · arXiv
  127. Nicholas P. Tatonetti, J. C. Denny, S. N. Murphy, G. H. Fernald, G. Krishnan, V. Castro, P. Yue, P. S. Tsau, I. Kohane, D. M. Roden, et al. 2011. Detecting drug interactions from adverse-event reports: Interaction between paroxetine and pravastatin increases blood glucose levels. Clin. Pharmacol. Therapeut. 90, 1 (2011), 133-142.
  128. Jonathan M. Teich. 1998. Information systems support for emergency medicine. Ann. Emerg. Med. 31, 3 (1998), 304- 307.
  129. Lucas Theis, Aäron van den Oord, and Matthias Bethge. 2015. A note on the evaluation of generative models. arXiv:1511.01844. Retrieved from https://arxiv.org/abs/1511.01844 · arXiv
  130. Dmitrii Usynin, Alexander Ziller, Marcus Makowski, Rickmer Braren, Daniel Rueckert, Ben Glocker, Georgios Kaissis, and Jonathan Passerat-Palmbach. 2021. Adversarial interference and its mitigations in privacy-preserving collabora- tive machine learning. Nat. Mach. Intell. 3, 9 (2021), 749-758.
  131. SS Vallender. 1974. Calculation of the Wasserstein distance between probability distributions on the line. Theory Probab. Appl. 18, 4 (1974), 784-786.
  132. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998-6008.
  133. Jason Walonoski, Sybil Klaus, Eldesia Granger, Dylan Hall, Andrew Gregorowicz, George Neyarapally, Abigail Wat- son, and Jeff Eastman. 2020. Synthea™ Novel coronavirus (COVID-19) model and synthetic data set. Intell.-Bas. Med. 1 (2020), 100007.
  134. Lu Wang, Wei Zhang, and Xiaofeng He. 2019. Continuous patient-centric sequence generation via sequentially cou- pled adversarial learning. In Database Systems for Advanced Applications, Guoliang Li, Jun Yang, Joao Gama, Jug- gapong Natwichai, and Yongxin Tong (Eds.). Springer International Publishing, Cham, 36-52.
  135. Shuo Wang, Carsten Rudolph, Surya Nepal, Marthie Grobler, and Shangyu Chen. 2020. PART-GAN: Privacy- preserving time-series sharing. In International Conference on Artificial Neural Networks. Springer, 578-593.
  136. John Weldon, Tomas Ward, and Eoin Brophy. 2021. Generation of synthetic electronic health records using a feder- ated GAN. arXiv:2109.02543. Retrieved from https://arxiv.org/abs/2109.02543 · arXiv
  137. Chih M. Wong, Nathaniel M. Hawkins, Mark C. Petrie, Pardeep S. Jhund, Roy S. Gardner, Cono A. Ariti, Katrina K. Poppe, Nikki Earle, Gillian A. Whalley, Iain B. Squire, et al. 2014. Heart failure in younger patients: The Meta-analysis Global Group in Chronic Heart Failure (MAGGIC). Eur. Heart J. 35, 39 (2014), 2714-2721.
  138. Mi-Ja Woo, Jerome P. Reiter, Anna Oganian, and Alan F. Karr. 2009. Global measures of data utility for microdata masked for disclosure limitation. J. Priv. Confident. 1, 1 (2009).
  139. Cao Xiao, Edward Choi, and Jimeng Sun. 2018. Opportunities and challenges in developing deep learning models using electronic health records data: A systematic review. J. Am. Med. Inf. Assoc. 25, 10 (2018), 1419-1428.
  140. Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou. 2018. Differentially private generative adversarial network. arXiv:1802.06739. Retrieved from https://arxiv.org/abs/1802.06739 · arXiv
  141. Alexandre Yahi, Rami Vanguri, Noémie Elhadad, and Nicholas P. Tatonetti. 2017. Generative adversarial networks for electronic health records: A framework for exploring and evaluating methods for predicting drug-induced laboratory test trajectories. arXiv:1712.00164. Retrieved from https://arxiv.org/abs/1712.00164 · arXiv
  142. Andrew Yale, Saloni Dash, Karan Bhanot, Isabelle Guyon, John S. Erickson, and Kristin P. Bennett. 2020. Synthesizing quality open data assets from private health research studies. In Business Information Systems Workshops: BIS '20 International Workshops, Revised Selected Papers 23. Springer, 324-335.
  143. Andrew Yale, Saloni Dash, Ritik Dutta, Isabelle Guyon, Adrien Pavao, and Kristin P. Bennett. 2020. Generation and evaluation of privacy preserving synthetic health data. Neurocomputing 416 (2020), 244-255.
  144. Chao Yan, Ziqi Zhang, Steve Nyemba, and Bradley A. Malin. 2020. Generating electronic health records with mul- tiple data types and constraints. In AMIA Annual Symposium Proceedings, Vol. 2020. American Medical Informatics Association, 1335.
  145. Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, and Honglak Lee. 2019. Diversity-sensitive condi- tional generative adversarial networks. arXiv:1901.09024. Retrieved from https://arxiv.org/abs/1901.09024 · arXiv
  146. Fan Yang, Zhongping Yu, Yunfan Liang, Xiaolu Gan, Kaibiao Lin, Quan Zou, and Yifeng Zeng. 2019. Grouped correla- tional generative adversarial networks for discrete electronic health records. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM '19). IEEE, 906-913.
  147. Yun Yang, Fengtao Nan, Po Yang, Qiang Meng, Yingfu Xie, Dehai Zhang, and Khan Muhammad. 2019. GAN-based semi-supervised learning approach for clinical decision support in health-IoT platform. IEEE Access 7 (2019), 8048- 8057.
  148. Yinchong Yang, Zhiliang Wu, Volker Tresp, and Peter A. Fasching. 2019. Categorical EHR imputation with generative adversarial nets. In Proceedings of the IEEE International Conference on Healthcare Informatics (ICHI '19). IEEE, 1-10.
  149. Xin Yi and Paul Babyn. 2018. Sharpness-aware low-dose CT denoising using conditional generative adversarial net- work. J. Digit. Imag. 31, 5 (2018), 655-669.
  150. Jinsung Yoon, Lydia N. Drumright, and Mihaela Van Der Schaar. 2020. Anonymization through data synthesis using generative adversarial networks (ads-gan). IEEE J. Biomed. Health Inf. 24, 8 (2020), 2378-2388.
  151. Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar. 2019. Time-series generative adversarial networks. Adv. Neural Inf. Process. Syst. 32 (2019).
  152. Jinsung Yoon, James Jordon, and Mihaela Schaar. 2018. GAIN: Missing data imputation using generative adversarial nets. In International Conference on Machine Learning. PMLR, 5689-5698.
  153. Jinsung Yoon, James Jordon, and Mihaela Schaar. 2018. RadialGAN: Leveraging multiple datasets to improve target- specific predictive models using Generative Adversarial Networks. In International Conference on Machine Learning. PMLR, 5699-5707.
  154. Jinsung Yoon, James Jordon, and Mihaela van der Schaar. 2018. GANITE: Estimation of individualized treatment effects using generative adversarial nets. In International Conference on Learning Representations.
  155. Michael P. Young, Valerie J. Gooder, Karen McBride, Brent James, and Elliott S. Fisher. 2003. Inpatient transfers to the intensive care unit. J. Gen. Internal Med. 18, 2 (2003), 77-83.
  156. Kezi Yu, Yunlong Wang, Yong Cai, Cao Xiao, Emily Zhao, Lucas Glass, and Jimeng Sun. 2019. Rare disease detection by sequence modeling with generative adversarial networks. arXiv:1907.01022. Retrieved from https://arxiv.org/abs/ 1907.01022 · arXiv
  157. Shuangfei Zhai, Yu Cheng, Rogerio Feris, and Zhongfei Zhang. 2016. Generative adversarial networks as variational training of energy based models. arXiv:1611.01799. Retrieved from https://arxiv.org/abs/1611.01799 · arXiv
  158. Chengqi Zhang and Shichao Zhang. 2003. Association Rule Mining: Models and Algorithms. Vol. 2307. Springer.
  159. Da Zhang, Ming Ma, and Likun Xia. 2022. A comprehensive review on GANs for time-series signals. Neural Comput. Appl. (2022), 1-21.
  160. Yue Zhang, Shun Miao, Tommaso Mansi, and Rui Liao. 2020. Unsupervised X-ray image segmentation with task driven generative adversarial networks. Med. Image Anal. 62 (2020), 101664.
  161. Zhifei Zhang, Yang Song, and Hairong Qi. 2018. Decoupled learning for conditional adversarial networks. In Pro- ceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV '18). IEEE, 700-708.
  162. Ziqi Zhang, Chao Yan, Thomas A. Lasko, Jimeng Sun, and Bradley A. Malin. 2021. SynTEG: A framework for temporal structured electronic health data simulation. J. Am. Med. Inf. Assoc. 28, 3 (2021), 596-604.
  163. Ziqi Zhang, Chao Yan, Diego A. Mesa, Jimeng Sun, and Bradley A. Malin. 2020. Ensuring electronic medical record simulation through better training, modeling, and evaluation. J. Am. Med. Inf. Assoc. 27, 1 (2020), 99-108.
  164. Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle- consistent adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision. 2223-2232.
  165. Xiaojin Zhu and Andrew B. Goldberg. 2009. Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3, 1 (2009), 1-130.

Cited by

Sorted by citations

Authors
Ghosheh, G. O., Li, J., & Zhu, T.
Affiliations
  • University of Oxford (GB)
  • Nanjing University of Information Science and Technology (CN)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2024-01-22
Volume
56
Issue
6
Pages
1-34
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3636424
OpenAlex
W4389386858
Cited by
95
References
23
Citations by year
2026: 17 · 2025: 55 · 2024: 16 · 2023: 6 · 2022: 1
FWCI
8.86
Type
journal article
Language
en
Open access
Licence
Locations
  • ACM Computing Surveys (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China
  • RAEng Engineering for Development Research Fellowship
  • National Institute for Health Research Oxford Biomedical Research Centre
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords
generative adversarial networks, synthetic electronic health records, data privacy, benchmarking metrics, benchmark dataset, clinical research data

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review