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. 2021 May 18;35(12):10469-10477.

Variational Disentanglement for Rare Event Modeling

Zidi Xiu  1 , Chenyang Tao  1 , Michael Gao  1 , Connor Davis  2 , Benjamin A Goldstein  1 , Ricardo Henao  1
Affiliations

Variational Disentanglement for Rare Event Modeling

Zidi Xiu et al. Proc AAAI Conf Artif Intell. .

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 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.

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Figures

Figure 1:
Figure 1:
Left: Distribution of a two-dimensional latent space z where the long tail associates with higher risk. Right: Tail estimations with different schemes for the long-tailed data in one-dimensional space. EVT provides more accurate characterization comparing to other mechanisms.
Figure 2:
Figure 2:
First latent dimension from the inp dataset (1% event rate). Left: Learned prior and posterior distribution, and monotonic predicted risks (right axis). Right: The latent representation values distribution grouped by event type.
Figure 3:
Figure 3:
Bootstrapped AUC (left) and AUPRC (right) distributions for the COVID mortality data (2.6% event rate).
See this image and copyright information in PMC

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References

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