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Methods for generating and evaluating synthetic longitudinal patient data: a systematic review
March 7, 2024, 5:43 a.m. | Katariina Perkonoja, Kari Auranen, Joni Virta
cs.LG updates on arXiv.org arxiv.org
Abstract: The proliferation of data in recent years has led to the advancement and utilization of various statistical and deep learning techniques, thus expediting research and development activities. However, not all industries have benefited equally from the surge in data availability, partly due to legal restrictions on data usage and privacy regulations, such as in medicine. To address this issue, various statistical disclosure and privacy-preserving methods have been proposed, including the use of synthetic data generation. …
abstract advancement arxiv availability cs.cr cs.lg data deep learning deep learning techniques development however industries patient research research and development review stat.ap statistical stat.me synthetic type
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