文章基本信息

标题：Synthetic data as an enabler for machine learning applications in medicine
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作者：Jean-Francois Rajotte ; Robert Bergen ; David L. Buckeridge 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：11
页码：1-9
DOI：10.1016/j.isci.2022.105331
语种：English
出版社：Elsevier
摘要：SummarySynthetic data generation is the process of using machine learning methods to train a model that captures the patterns in a real dataset. Then new or synthetic data can be generated from that trained model. The synthetic data does not have a one-to-one mapping to the original data or to real patients, and therefore has the potential of privacy preserving properties. There is a growing interest in the application of synthetic data across health and life sciences, but to fully realize the benefits, further education, research, and policy innovation is required. This article summarizes the opportunities and challenges of SDG for health data, and provides directions for how this technology can be leveraged to accelerate data access for secondary purposes.Graphical abstractDisplay OmittedArtificial intelligence; Artificial intelligence applications; Health sciences; Medical science