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. 2019 Mar 1;26(3):228-241.
doi: 10.1093/jamia/ocy142.

Synthesizing electronic health records using improved generative adversarial networks

Mrinal Kanti Baowaly  1   2 , Chia-Ching Lin  3   4 , Chao-Lin Liu  2 , Kuan-Ta Chen  4
Affiliations

Synthesizing electronic health records using improved generative adversarial networks

Mrinal Kanti Baowaly et al. J Am Med Inform Assoc. .

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.

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Figures

Figure 1.
Figure 1.
ECDFs of ICD codes and patients for MIMIC-III, extended MIMIC-III, and NHIRD datasets.
Figure 2.
Figure 2.
Original GAN and medGAN architecture.
Figure 3.
Figure 3.
Scatterplots of dimension-wise probability results of real binary data (x-axis) vs. synthetic counterpart (y-axis) produced by the 3 generative models.
Figure 4.
Figure 4.
Scatterplots of dimension-wise average count results on real count data (x-axis) vs. synthetic counterpart (y-axis) produced by the 3 generative models.
Figure 5.
Figure 5.
Scatterplots of dimension-wise prediction results (F1-scores) of logistic regression model trained on real binary data (x-axis) vs. synthetic counterpart (y-axis) produced by the 3 generative models.
Figure 6.
Figure 6.
Scatterplots of dimension-wise prediction results (F1-scores) of logistic regression model trained on real count data (x-axis) vs. synthetic counterpart (y-axis) produced by the 3 generative models.
See this image and copyright information in PMC

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