Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks
This addresses the challenge of improving diagnosis for rare disease patients, but it is incremental as it applies existing deep learning methods to a specific medical domain.
The paper tackled the problem of detecting exocrine pancreatic insufficiency (EPI), a rare disease, from longitudinal medical claims data, achieving a PR-AUC of 0.56 that outperformed benchmark models.
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.