论文标题
通过一致性指数的半参数，参数和机器学习模型的实验比较
Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index
论文作者
论文摘要
在本文中，我们对半参数（COX比例危害模型，AALEN的加性回归模型），参数（Weibull AFT模型）和机器学习模型进行实验比较（随机生存森林，渐变森林，与COX相比危害损失，DeepSURV，DeepsUrv）通过两个不同的数据集合（PBC）（PBC和GBC）（gbc和gbc）。我们提出了两个比较：一个与这些模型的默认超参数，另一个与随机搜索发现的最佳超参数。
In this paper, we make an experimental comparison of semi-parametric (Cox proportional hazards model, Aalen's additive regression model), parametric (Weibull AFT model), and machine learning models (Random Survival Forest, Gradient Boosting with Cox Proportional Hazards Loss, DeepSurv) through the concordance index on two different datasets (PBC and GBCSG2). We present two comparisons: one with the default hyper-parameters of these models and one with the best hyper-parameters found by randomized search.
