  Rare diseases affect hundreds of millions of people worldwide but are hard to
detect since they have extremely low prevalence rates (varying from 1/1,000 to
1/200,000 patients) and are massively underdiagnosed. How do we reliably detect
rare diseases with such low prevalence rates? How to further leverage patients
with possibly uncertain diagnosis to improve detection? In this paper, we
propose a Complementary pattern Augmentation (CONAN) framework for rare disease
detection. CONAN combines ideas from both adversarial training and max-margin
classification. It first learns self-attentive and hierarchical embedding for
patient pattern characterization. Then, we develop a complementary generative
adversarial networks (GAN) model to generate candidate positive and negative
samples from the uncertain patients by encouraging a max-margin between
classes. In addition, CONAN has a disease detector that serves as the
discriminator during the adversarial training for identifying rare diseases. We
evaluated CONAN on two disease detection tasks. For low prevalence inflammatory
bowel disease (IBD) detection, CONAN achieved .96 precision recall area under
the curve (PR-AUC) and 50.1% relative improvement over best baseline. For rare
disease idiopathic pulmonary fibrosis (IPF) detection, CONAN achieves .22
PR-AUC with 41.3% relative improvement over the best baseline.
