Generation of Synthetic Electronic Health Records Using a Federated GAN

TL;DR

This paper presents a federated GAN approach to generate synthetic electronic health records, enabling data sharing across hospitals without exposing sensitive patient data, and demonstrates comparable quality to centralized models.

Contribution

It introduces a federated learning framework for GANs to generate high-quality synthetic EHR data from multiple data silos without data sharing.

Findings

Synthetic data quality is maintained across federated and centralized training.

Statistical evaluation shows minimal RMSE difference between methods.

Medical professionals' review confirms comparable data realism.

Abstract

Sensitive medical data is often subject to strict usage constraints. In this paper, we trained a generative adversarial network (GAN) on real-world electronic health records (EHR). It was then used to create a data-set of "fake" patients through synthetic data generation (SDG) to circumvent usage constraints. This real-world data was tabular, binary, intensive care unit (ICU) patient diagnosis data. The entire data-set was split into separate data silos to mimic real-world scenarios where multiple ICU units across different hospitals may have similarly structured data-sets within their own organisations but do not have access to each other's data-sets. We implemented federated learning (FL) to train separate GANs locally at each organisation, using their unique data silo and then combining the GANs into a single central GAN, without any siloed data ever being exposed. This global, central GAN was then used to generate the synthetic patients data-set. We performed an evaluation of these synthetic patients with statistical measures and through a structured review by a group of medical professionals. It was shown that there was no significant reduction in the quality of the synthetic EHR when we moved between training a single central model and training on separate data silos with individual models before combining them into a central model. This was true for both the statistical evaluation (Root Mean Square Error (RMSE) of 0.0154 for single-source vs. RMSE of 0.0169 for dual-source federated) and also for the medical professionals' evaluation (no quality difference between EHR generated from a single source and EHR generated from multiple sources).