A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
arxiv(2022)
摘要
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical
research and point of care applications; however, many challenges such as data
privacy concerns impede its optimal utilization. Deep generative models,
particularly, Generative Adversarial Networks (GANs) show great promise in
generating synthetic EHR data by learning underlying data distributions while
achieving excellent performance and addressing these challenges. This work aims
to review the major developments in various applications of GANs for EHRs and
provides an overview of the proposed methodologies. For this purpose, we
combine perspectives from healthcare applications and machine learning
techniques in terms of source datasets and the fidelity and privacy evaluation
of the generated synthetic datasets. We also compile a list of the metrics and
datasets used by the reviewed works, which can be utilized as benchmarks for
future research in the field. We conclude by discussing challenges in GANs for
EHRs development and proposing recommended practices. We hope that this work
motivates novel research development directions in the intersection of
healthcare and machine learning.
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关键词
generative adversarial networks,electronic health
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