DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis
DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis
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DOI：
10.1609/aaai.v35i1.16125
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
M. Rahman;K. Matsuo;S. Matsuzaki;S. Purushotham
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文献类型：
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作者：
M. Rahman;K. Matsuo;S. Matsuzaki;S. Purushotham
竞争风险分析(CRA)的目的是在竞争事件存在的情况下，正确估计事件发生的边际概率。为CRA开发的许多统计方法都受到关于潜在随机过程的强烈假设的限制。为了克服这些问题并处理审查问题，CRA的机器学习方法设计了专门的成本函数。然而，这些方法不是可推广的，并且计算代价很高。本文将CRA描述为一个特定原因的回归问题，并提出了深度伪模型，该模型使用简单有效的前馈深度神经网络，利用基于Aalen-Johansen估计的伪值来预测累积关联函数(CIF)。深度伪模型在处理删失观测数据时，捕捉了CIF的时变协变量效应。我们展示了深度伪模型如何通过使用修改的伪值来解决协变量相依截尾问题。在真实和合成数据集上的实验表明，与最先进的CRA方法相比，我们提出的模型获得了有希望的和统计上有意义的结果。此外，我们还表明，可解释的方法，如分层相关传播，可以用来解释我们的深度伪模型的预测。
Competing Risk Analysis (CRA) aims at the correct estimation of the marginal probability of occurrence of an event in the presence of competing events. Many of the statistical approaches developed for CRA are limited by strong assumptions about the underlying stochastic processes. To overcome these issues and to handle censoring, machine learning approaches for CRA have designed specialized cost functions. However, these approaches are not generalizable, and are computationally expensive. This paper formulates CRA as a cause-specific regression problem and proposes DeepPseudo models, which use simple and effective feed-forward deep neural networks, to predict the cumulative incidence function (CIF) using Aalen-Johansen estimator-based pseudo values. DeepPseudo models capture the time-varying covariate effect on CIF while handling the censored observations. We show how DeepPseudo models can address co-variate dependent censoring by using modified pseudo values. Experiments on real and synthetic datasets demonstrate that our proposed models obtain promising and statistically significant results compared to the state-of-the-art CRA approaches. Furthermore, we show that explainable methods such as Layer-wise Relevance Propagation can be used to interpret the predictions of our DeepPseudo models.