  Rare diseases affecting 350 million individuals are commonly associated with
delay in diagnosis or misdiagnosis. To improve those patients' outcome, rare
disease detection is an important task for identifying patients with rare
conditions based on longitudinal medical claims. In this paper, we present a
deep learning method for detecting patients with exocrine pancreatic
insufficiency (EPI) (a rare disease). The contribution includes 1) a large
longitudinal study using 7 years medical claims from 1.8 million patients
including 29,149 EPI patients, 2) a new deep learning model using generative
adversarial networks (GANs) to boost rare disease class, and also leveraging
recurrent neural networks to model patient sequence data, 3) an accurate
prediction with 0.56 PR-AUC which outperformed benchmark models in terms of
precision and recall.
