  Generating datasets that "look like" given real ones is an interesting tasks
for healthcare applications of ML and many other fields of science and
engineering. In this paper we propose a new method of general application to
binary datasets based on a method for learning the parameters of a latent
variable moment that we have previously used for clustering patient datasets.
We compare our method with a recent proposal (MedGan) based on generative
adversarial methods and find that the synthetic datasets we generate are
globally more realistic in at least two senses: real and synthetic instances
are harder to tell apart by Random Forests, and the MMD statistic. The most
likely explanation is that our method does not suffer from the "mode collapse"
which is an admitted problem of GANs. Additionally, the generative models we
generate are easy to interpret, unlike the rather obscure GANs. Our experiments
are performed on two patient datasets containing ICD-9 diagnostic codes: the
publicly available MIMIC-III dataset and a dataset containing admissions for
congestive heart failure during 7 years at Hospital de Sant Pau in Barcelona.
