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Deep learning for survival outcomes.
Statistics in Medicine ( IF 2 ) Pub Date : 2020-04-13 , DOI: 10.1002/sim.8542
Jon Arni Steingrimsson 1 , Samantha Morrison 1
Affiliation  

Deep learning is a class of machine learning algorithms that are popular for building risk prediction models. When observations are censored, the outcomes are only partially observed and standard deep learning algorithms cannot be directly applied. We develop a new class of deep learning algorithms for outcomes that are potentially censored. To account for censoring, the unobservable loss function used in the absence of censoring is replaced by a censoring unbiased transformation. The resulting class of algorithms can be used to estimate both survival probabilities and restricted mean survival. We show how the deep learning algorithms can be implemented by adapting software for uncensored data by using a form of response transformation. We provide comparisons of the proposed deep learning algorithms to existing risk prediction algorithms for predicting survival probabilities and restricted mean survival through both simulated datasets and analysis of data from breast cancer patients.

中文翻译:

深度学习的生存结果。

深度学习是一类机器学习算法,常用于构建风险预测模型。当观察结果被审查时,只能观察到部分结果,并且无法直接应用标准深度学习算法。我们针对可能受到审查的结果开发了一类新的深度学习算法。为了考虑审查,在没有审查的情况下使用的不可观察的损失函数被审查无偏变换所取代。由此产生的一类算法可用于估计生存概率和受限平均生存率。我们展示了如何通过使用响应变换的形式使软件适应未经审查的数据来实现深度学习算法。我们将所提出的深度学习算法与现有的风险预测算法进行比较,通过模拟数据集和对乳腺癌患者数据的分析来预测生存概率和限制平均生存率。
更新日期:2020-04-13
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