  The widespread adoption of electronic health records (EHRs) and subsequent
increased availability of longitudinal healthcare data has led to significant
advances in our understanding of health and disease with direct and immediate
impact on the development of new diagnostics and therapeutic treatment options.
However, access to EHRs is often restricted due to their perceived sensitive
nature and associated legal concerns, and the cohorts therein typically are
those seen at a specific hospital or network of hospitals and therefore not
representative of the wider population of patients. Here, we present HealthGen,
a new approach for the conditional generation of synthetic EHRs that maintains
an accurate representation of real patient characteristics, temporal
information and missingness patterns. We demonstrate experimentally that
HealthGen generates synthetic cohorts that are significantly more faithful to
real patient EHRs than the current state-of-the-art, and that augmenting real
data sets with conditionally generated cohorts of underrepresented
subpopulations of patients can significantly enhance the generalisability of
models derived from these data sets to different patient populations. Synthetic
conditionally generated EHRs could help increase the accessibility of
longitudinal healthcare data sets and improve the generalisability of
inferences made from these data sets to underrepresented populations.
