  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 events, and
develop a robust prediction arm that joins the merits of the generalized
additive model and isotonic neural nets. Results on synthetic studies and
diverse real-world datasets, including mortality prediction on a COVID-19
cohort, demonstrate that the proposed approach outperforms existing
alternatives.
