Synthetic electronic health records generated with variational graph autoencoders.

ABSTRACT: Data-driven medical care delivery must always respect patient privacy-a requirement that is not easily met. This issue has impeded improvements to healthcare software and has delayed the long-predicted prevalence of artificial intelligence in healthcare. Until now, it has been very difficult to share data between healthcare organizations, resulting in poor statistical models due to unrepresentative patient cohorts. Synthetic data, i.e., artificial but realistic electronic health records, could overcome the drought that is troubling the healthcare sector. Deep neural network architectures, in particular, have shown an incredible ability to learn from complex data sets and generate large amounts of unseen data points with the same statistical properties as the training data. Here, we present

SUBMITTER: Nikolentzos G 

PROVIDER: S-EPMC10148837 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature