Synthesizing electronic health records using improved generative adversarial networks
Synthesizing electronic health records using improved generative adversarial networks
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DOI：
10.1093/jamia/ocy142
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发表时间：
2019-03-01
影响因子：
6.4
通讯作者：
Chen, Kuan-Ta
中科院分区：
文献类型：
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作者：
Baowaly, Mrinal Kanti;Lin, Chia-Ching;Chen, Kuan-Ta
目的：本研究的目的是生成合成电子健康记录(EHR)。生成的EHR数据将比现有的医疗生成对抗网络(MedGAN)方法生成的数据更真实。材料和方法：我们对MedGAN方法进行了修改，得到了两种合成数据生成模型-医学Wasserstein GAN和医学边界搜索GAN(MedBGAN)，并对三种模型的结果进行了比较。我们使用了两个数据库：台湾的MIMIC-III和国家健康保险研究数据库(NHIRD)。首先，我们对模型进行训练，并使用这三个模型生成合成的EHR。然后使用几种统计方法(Kolmogorov-Smirnov检验、二进制数据的维度概率和计数数据的维度平均计数)和两个机器学习任务(关联规则挖掘和预测)对模型的性能进行了分析和比较。结果：我们进行了全面的分析，发现我们的模型对于生成合成的EHR数据是足够有效的。所提出的模型在所有情况下都优于MedGAN，其中边界寻觅GaN(MedBGAN)模型的表现最好。讨论：为了生成真实的合成电子病历数据，从提供更好服务的角度来看，所提出的模型将在医疗行业和相关研究中有效。结论：所提出的模型能够充分了解真实电子病历的数据分布，有效地生成真实的合成电子病历。结果表明，与已有模型相比，该模型具有一定的优越性。
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.