Modelling Patient Sequences for Rare Disease Detection with Semisupervised Generative Adversarial Nets. Rare diseases affect 350 million patients worldwide, but they are commonly delayed in diagnosis or misdiagnosed. The problem of detecting rare disease faces two main challenges: the first being extreme imbalance of data and the second being finding the appropriate features. In this paper, we propose to address the problems by using semisupervised generative adversarial networks (GANs) to deal with the data imbalance issue and recurrent neural networks (RNNs) to directly model patient sequences. We experimented with detecting patients with a particular rare disease (exocrine pancreatic insufficiency, EPI). The dataset includes 1.8 million patients with 29,149 patients being positive, from a large longitudinal study using 7 years medical claims. Our model achieved 0.56 PRAUC and outperformed benchmark models in terms of precision and recall.

Grouped Correlational Generative Adversarial Networks for Discrete Electronic Health Records Using Generative Adversarial Networks (GANs) to generate synthetic Electronic Health Records (EHR) has attracted increasing attention. However, in existing approaches, the events in EHRs are treated as separate variables which are indiscriminately entered into the model, without taking into account the meaning and grouping of them. Besides, the efficacy of treatment is often neglected. In this paper, we first embed the efficacy information into the disease diagnosis, and then propose Grouped Correlational GAN (GcGAN) to explicitly learn inherent correlations between different groups of variables. We also introduce a dense connection to strengthen the generator capacity in GcGAN. Experimental results on realworld data demonstrate that the generated data from GcGAN are able to simulate realworld data in terms of distribution statistics. The results on multilabel treatment recommendation tasks show that GcGAN can boost the performances by augmenting the training dataset with the generated data and outperforms stateoftheart approaches. It can also automatically distinguish between diseasespecific drugs and adjuvant drugs, which enhances the model interpretability.