  Synthetic medical data which preserves privacy while maintaining utility can
be used as an alternative to real medical data, which has privacy costs and
resource constraints associated with it. At present, most models focus on
generating cross-sectional health data which is not necessarily representative
of real data. In reality, medical data is longitudinal in nature, with a single
patient having multiple health events, non-uniformly distributed throughout
their lifetime. These events are influenced by patient covariates such as
comorbidities, age group, gender etc. as well as external temporal effects
(e.g. flu season). While there exist seminal methods to model time series data,
it becomes increasingly challenging to extend these methods to medical event
time series data. Due to the complexity of the real data, in which each patient
visit is an event, we transform the data by using summary statistics to
characterize the events for a fixed set of time intervals, to facilitate
analysis and interpretability. We then train a generative adversarial network
to generate synthetic data. We demonstrate this approach by generating human
sleep patterns, from a publicly available dataset. We empirically evaluate the
generated data and show close univariate resemblance between synthetic and real
data. However, we also demonstrate how stratification by covariates is required
to gain a deeper understanding of synthetic data quality.
