Variational Disentanglement for Rare Event Modeling
Variational Disentanglement for Rare Event Modeling
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DOI：
10.1609/aaai.v35i12.17253
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Zidi Xiu;Chenyang Tao;M. Gao;Connor Davis;B. Goldstein;Ricardo Henao
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作者：
Zidi Xiu;Chenyang Tao;M. Gao;Connor Davis;B. Goldstein;Ricardo Henao
医疗保健数据的可用性和丰富性以及机器学习方法的当前进展相结合，为改善临床决策支持系统创造了新的机会。然而，在医疗保健风险预测应用中，相对于可用的样本量，具有感兴趣的病症（标签）的病例的比例通常非常低。虽然在医疗保健中非常普遍，但这种不平衡的分类设置在许多其他场景中也很常见且具有挑战性。因此，出于动机，我们提出了一个变分解纠缠的方法，半参数学习罕见的事件在严重不平衡的分类问题。具体来说，我们利用潜在空间上的极端分布行为来从低流行率事件中提取信息，并开发一个强大的预测臂，该预测臂结合了广义加性模型和保序神经网络的优点。综合研究和各种真实世界数据集的结果，包括对COVID-19队列的死亡率预测，表明所提出的方法优于现有的替代方法。
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.