Clinical data sharing using Generative Adversarial Networks

Abstract

Obtaining data is challenging for researchers, especially when it comes to medical data. Moreover, using medical data as there are concerns about privacy and confidentiality issues requires specific considerations. Generative models aim to learn data distribution via various statistical learning approaches. Among generative models, a machine learning-based approach named Generative Adversarial Networks (GANs) has proved their potential in the implicit density estimation of high dimensional data. Therefore, we suggest an approach that each healthcare organization, especially hospitals, could create and share their own GAN model, entitled Hospital-Based GANs (H-GANs), instead of sharing raw data of patients.