Variational Learning of Individual Survival Distributions.
Variational Learning of Individual Survival Distributions.
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DOI：
10.1145/3368555.3384454
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Henao R
中科院分区：
文献类型：
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作者：
Xiu Z;Tao C;Henao R
丰富的现代健康数据为使用机器学习技术构建更好的统计模型以改善临床决策提供了许多机会。预测事件发生时间分布，也称为生存分析，在许多临床应用中起着关键作用。我们介绍了一种变分时间到事件预测模型，称为变分生存推理（VSI），它建立在分布学习技术和深度神经网络的最新进展之上。VSI解决了非参数分布估计的挑战，通过（i）放松经典模型中的限制性建模假设，以及（ii）有效地处理删失观测，即，发生在观测窗口之外的事件，都在变分框架内。为了验证我们方法的有效性，我们在合成数据集和真实世界数据集上进行了一系列广泛的实验，显示出相对于竞争解决方案的性能改进。
The abundance of modern health data provides many opportunities for the use of machine learning techniques to build better statistical models to improve clinical decision making. Predicting time-to-event distributions, also known as survival analysis, plays a key role in many clinical applications. We introduce a variational time-to-event prediction model, named Variational Survival Inference (VSI), which builds upon recent advances in distribution learning techniques and deep neural networks. VSI addresses the challenges of non-parametric distribution estimation by (i) relaxing the restrictive modeling assumptions made in classical models, and (ii) efficiently handling the censored observations, i.e., events that occur outside the observation window, all within the variational framework. To validate the effectiveness of our approach, an extensive set of experiments on both synthetic and real-world datasets is carried out, showing improved performance relative to competing solutions.