论文标题
生存混合物密度网络
Survival Mixture Density Networks
论文作者
论文摘要
生存分析是事实建模的艺术，在临床治疗决策中起着重要作用。最近，已经提出了由神经ODE建立的连续时间模型进行生存分析。然而，由于神经ode求解器的计算复杂性很高，神经ODE的训练很慢。在这里，我们提出了一种有效的替代方案，用于柔性连续时间模型，称为生存混合物密度网络（生存MDN）。生存MDN适用于混合密度网络（MDN）的输出的可逆阳性功能。尽管MDN产生灵活的实价分布，但可逆正函数将模型映射到时间域，同时保留可拖动的密度。使用四个数据集，我们表明生存MDN的性能要比在一致性上的连续和离散时间基准相似，或类似于一致的二项式log-logikelihoody。同时，生存MDN的速度也比基于ODE的模型和离散模型中规避的分类问题快。
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.
