Synthesizing electronic health records using improved generative adversarial networks. Baowaly, M. K., Lin, C., Liu, C., & Chen, K. J. Am. Med. Inform. Assoc., 26(3):228–241, 2019.
Synthesizing electronic health records using improved generative adversarial networks [link]Paper  doi  abstract   bibtex   
Objective: The aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method. Materials and Methods: We modified medGAN to obtain two synthetic data generation models-designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)-and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First, we trained the models and generated synthetic EHRs by using these three 3 models. We then analyzed and compared the models' performance by using a few statistical methods (Kolmogorov-Smirnov test, dimension-wise probability for binary data, and dimension-wise average count for count data) and 2 machine learning tasks (association rule mining and prediction). Results: We conducted a comprehensive analysis and found our models were adequately efficient for generating synthetic EHR data. The proposed models outperformed medGAN in all cases, and among the 3 models, boundary-seeking GAN (medBGAN) performed the best. Discussion: To generate realistic synthetic EHR data, the proposed models will be effective in the medical industry and related research from the viewpoint of providing better services. Moreover, they will eliminate barriers including limited access to EHR data and thus accelerate research on medical informatics. Conclusion: The proposed models can adequately learn the data distribution of real EHRs and efficiently generate realistic synthetic EHRs. The results show the superiority of our models over the existing model.
@article{baowaly_synthesizing_2019,
	title = {Synthesizing electronic health records using improved generative adversarial networks},
	volume = {26},
	issn = {1067-5027},
	url = {http://dx.doi.org/10.1093/jamia/ocy142 https://www.ncbi.nlm.nih.gov/pubmed/30535151 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocy142 https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocy142/5235390},
	doi = {10.1093/jamia/ocy142},
	abstract = {Objective: The aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method. Materials and Methods: We modified medGAN to obtain two synthetic data generation models-designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)-and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First, we trained the models and generated synthetic EHRs by using these three 3 models. We then analyzed and compared the models' performance by using a few statistical methods (Kolmogorov-Smirnov test, dimension-wise probability for binary data, and dimension-wise average count for count data) and 2 machine learning tasks (association rule mining and prediction). Results: We conducted a comprehensive analysis and found our models were adequately efficient for generating synthetic EHR data. The proposed models outperformed medGAN in all cases, and among the 3 models, boundary-seeking GAN (medBGAN) performed the best. Discussion: To generate realistic synthetic EHR data, the proposed models will be effective in the medical industry and related research from the viewpoint of providing better services. Moreover, they will eliminate barriers including limited access to EHR data and thus accelerate research on medical informatics. Conclusion: The proposed models can adequately learn the data distribution of real EHRs and efficiently generate realistic synthetic EHRs. The results show the superiority of our models over the existing model.},
	number = {3},
	journal = {J. Am. Med. Inform. Assoc.},
	author = {Baowaly, Mrinal Kanti and Lin, Chia-Ching and Liu, Chao-Lin and Chen, Kuan-Ta},
	year = {2019},
	keywords = {Aggregate and Highlight, Boundary-seeking GAN (BGAN), CD GAN Review, CD GAN methods, Electronic health records (EHRs), F-Read, Generative adversarial networks (GANs), Review/Aggregates, Synthetic data generation (SDG), T-Aggregates, Wasserstein GAN with gradient penalty (WGAN-GP), roam},
	pages = {228--241},
}

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