Variational Disentanglement for Rare Event Modeling.

ABSTRACT: Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size. Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios. So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. Specifically, we leverage the imposed extreme-distribution behavior on a latent space to extract information from low-prevalence ev

SUBMITTER: Xiu Z 

PROVIDER: S-EPMC7523120 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature