Related papers: Variational Disentanglement for Rare Event Modeling

Variational Disentanglement for Rare Event Modeling

URL: http://arxiv.org/abs/2009.08541v5
Date: Wed, 16 Jun 2021 14:50:43 GMT
Title: Variational Disentanglement for Rare Event Modeling
Authors: Zidi Xiu, Chenyang Tao, Michael Gao, Connor Davis, Benjamin A. Goldstein, Ricardo Henao
Abstract summary: We propose a variational disentanglement approach to 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.
Score: 21.269897066024306
License: http://creativecommons.org/licenses/by/4.0/
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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