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.

通过对患者数据集进行聚类，我们提出了一种新方法来生成二进制数据集，该方法不同于 Generative adversarial networks（GANs），生成的模型易于解释，且通过比较实际应用数据集的多个参数得出的结果表明，该方法生成的人造数据集在多个指标上比现有方法更为逼真。