Survival Mixture Density Networks
Survival Mixture Density Networks
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DOI：
10.48550/arxiv.2208.10759
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Xintian Han;Mark Goldstein;R. Ranganath
中科院分区：
文献类型：
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作者：
Xintian Han;Mark Goldstein;R. Ranganath
生存分析是一种时间-事件建模的艺术，在临床治疗决策中起着重要作用。最近，连续时间模型建立的神经常微分方程已经提出了生存分析。然而，由于神经常微分方程求解器的高计算复杂度，神经常微分方程的训练是缓慢的。在这里，我们提出了一种灵活的连续时间模型的有效替代方案，称为生存混合密度网络（生存MDN）。生存MDN将可逆正函数应用于混合密度网络（MDN）的输出。虽然MDN产生灵活的实值分布，但可逆正函数将模型映射到时域，同时保持易于处理的密度。使用四个数据集，我们表明，生存MDN表现更好，或类似于连续和离散时间基线的一致性，综合Brier评分和综合二项式对数似然。同时，生存MDN也比基于ODE的模型更快，并避免了离散模型中的分箱问题。
Survival analysis, the art of time-to-event modeling, plays an important role in clinical treatment decisions. Recently, continuous time models built from neural ODEs have been proposed for survival analysis. However, the training of neural ODEs is slow due to the high computational complexity of neural ODE solvers. Here, we propose an efficient alternative for flexible continuous time models, called Survival Mixture Density Networks (Survival MDNs). Survival MDN applies an invertible positive function to the output of Mixture Density Networks (MDNs). While MDNs produce flexible real-valued distributions, the invertible positive function maps the model into the time-domain while preserving a tractable density. Using four datasets, we show that Survival MDN performs better than, or similarly to continuous and discrete time baselines on concordance, integrated Brier score and integrated binomial log-likelihood. Meanwhile, Survival MDNs are also faster than ODE-based models and circumvent binning issues in discrete models.