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    政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 >  Item 140.119/129121


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/129121


    Title: Synthesizing electronic health records using improved generative adversarial networks
    Authors: 劉昭麟
    Liu, Chao-Lin
    Baowaly*, Mrinal Kanti
    Lin, Chia-Ching
    Chen, Kuan-Ta
    Contributors: 資科系
    Keywords: electronic health records (EHRs); synthetic data generation (SDG); generative adversarial networks (GANs); Wasserstein GAN with gradient penalty (WGAN-GP); boundary-seeking GAN (BGAN)
    Date: 2019-03
    Issue Date: 2020-03-05 14:41:17 (UTC+8)
    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.
    Relation: Journal of the American Medical Informatics Association, Vol.26, No.3, pp.228–241
    Data Type: article
    DOI 連結: https://doi.org/10.1093/jamia/ocy142
    DOI: 10.1093/jamia/ocy142
    Appears in Collections:[資訊科學系] 期刊論文

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