Methods for generating and evaluating synthetic longitudinal patient data: a systematic review

arXiv:2309.12380v2 Announce Type: replace-cross
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. …

advancement arxiv availability cs.cr cs.lg data deep learning development equally industries led patient data proliferation research research and development review stat.ap stat.me synthetic techniques