  The recent availability of electronic health records (EHRs) have provided
enormous opportunities to develop artificial intelligence (AI) algorithms.
However, patient privacy has become a major concern that limits data sharing
across hospital settings and subsequently hinders the advances in AI. Synthetic
data, which benefits from the development and proliferation of generative
models, has served as a promising substitute for real patient EHR data.
However, the current generative models are limited as they only generate single
type of clinical data for a synthetic patient, i.e., either continuous-valued
or discrete-valued. To mimic the nature of clinical decision-making which
encompasses various data types/sources, in this study, we propose a generative
adversarial network (GAN) entitled EHR-M-GAN which simultaneously synthesizes
mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the
multidimensional, heterogeneous, and correlated temporal dynamics in patient
trajectories. We have validated EHR-M-GAN on three publicly-available intensive
care unit databases with records from a total of 141,488 unique patients, and
performed privacy risk evaluation of the proposed model. EHR-M-GAN has
demonstrated its superiority over state-of-the-art benchmarks for synthesizing
clinical timeseries with high fidelity, while addressing the limitations
regarding data types and dimensionality in the current generative models.
Notably, prediction models for outcomes of intensive care performed
significantly better when training data was augmented with the addition of
EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI
algorithms in resource-limited settings, lowering the barrier for data
acquisition while preserving patient privacy.
