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View all- Budu EEtminani KSoliman ARögnvaldsson T(2024)Evaluation of synthetic electronic health recordsNeurocomputing10.1016/j.neucom.2024.128253603:COnline publication date: 28-Oct-2024
Generating Longitudinal Synthetic EHR Data with Recurrent Autoencoders and Generative Adversarial Networks
Authors: 
Siao Sun, Fusheng Wang, Sina Rashidian, Tahsin Kurc, Kayley Abell-Hart, Janos Hajagos, Wei Zhu, Mary Saltz, Joel SaltzAuthors Info & Claims
Pages 153 - 165
Abstract
Synthetic electronic health records (EHR) can facilitate effective use of clinical data in software development, medical education, and medical research without the concerns of data privacy. We propose a novel Generative Adversarial Network (GAN) approach, called Longitudinal GAN (LongGAN), that can generate synthetic longitudinal EHR data. LongGAN employs a recurrent autoencoder and the Wasserstein GAN Gradient Penalty (WGAN-GP) architecture with conditional inputs. We evaluate LongGAN with the task of generating training data for machine/deep learning methods. Our experiments show that predictive models trained with synthetic data from LongGAN achieve comparable performance to those trained with real data. Moreover, these models have up to 0.27 higher AUROC and up to 0.21 higher AUPRC values than models trained with synthetic data from RCGAN and TimeGAN, the two most relevant methods for longitudinal data generation. We also demonstrate that LongGAN is able to preserve patient privacy in a given attribute disclosure attack setting.
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Published In
Aug 2021
191 pages
ISBN:978-3-030-93662-4
DOI:10.1007/978-3-030-93663-1
© Springer Nature Switzerland AG 2021.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 20 August 2021
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View all- Budu EEtminani KSoliman ARögnvaldsson T(2024)Evaluation of synthetic electronic health recordsNeurocomputing10.1016/j.neucom.2024.128253603:COnline publication date: 28-Oct-2024