BIP Group and AINDO have teamed up to present their whitepaper on synthetic data at the PHUSE Data Transparency Winter Event during the session taking place on February 8th from 15:00 to 17:30 (GMT). The event looks to meet the ongoing needs of data transparency within the clinical development arena and features thought-provoking presentations, panel discussions and Q&A sessions from experts in the data sharing field. Gabriele Oliva, Head of Data Analytics for BIP UK, and Sebastiano Saccani, Co-Founder & Head of R&D for AINDO, will talk about how healthcare enterprises can benefit from synthetic data before describing an innovative method for generating realistic synthetic relational data through graph variational autoencoders.
Here is a short summary of the research paper:
“Synthetic data generation has recently gained widespread attention as a more reliable alternative to traditional data anonymization. The involved methods are originally developed for image synthesis. Hence, their application to the typically tabular and relational datasets from healthcare, finance and other industries is nontrivial. While substantial research has been devoted to the generation of realistic tabular datasets, the study of synthetic relational databases is still in its infancy. In this paper, we combine the variational autoencoder framework with graph neural networks to generate realistic synthetic relational databases. We then apply the obtained method to two publicly available databases in computational experiments. The results indicate that real databases’ structures are accurately preserved in the resulting synthetic datasets, even for large datasets with advanced data types.”
