Generation of realistic synthetic data using Multimodal Neural Ordinary Differential Equations.

ABSTRACT: Individual organizations, such as hospitals, pharmaceutical companies, and health insurance providers, are currently limited in their ability to collect data that are fully representative of a disease population. This can, in turn, negatively impact the generalization ability of statistical models and scientific insights. However, sharing data across different organizations is highly restricted by legal regulations. While federated data access concepts exist, they are technically and organizationally difficult to realize. An alternative approach would be to exchange synthetic patient data instead. In this work, we introduce the Multimodal Neural Ordinary Differential Equations (MultiNODEs), a hybrid, multimodal AI approach, which allows for generating highly realistic synthetic patient tra

SUBMITTER: Wendland P 

PROVIDER: S-EPMC9391444 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature