深度核Aalen-Johansen估计器：一种可解释且灵活的神经网络框架用于竞争风险
Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks
AI总结：
本文提出了一种基于深度学习的可解释竞争风险模型，通过核函数学习权重来预测累积发病率函数，并在多个数据集上验证了其解释能力。
摘要：
我们提出了一种可解释的深度竞争风险模型，称为深度核Aalen-Johansen（DKAJ）估计器，该模型扩展了经典的Aalen-Johansen非参数累积发病率函数（CIFs）估计方法。每个数据点（例如患者）被表示为聚类的加权组合。如果一个数据点仅在一个聚类中有非零权重，则其预测的CIFs对应于经典Aalen-Johansen估计器限制在该聚类数据点上的结果。这些权重来自一个自动学习的核函数，用于衡量任意两个数据点的相似性。在四个标准竞争风险数据集上，我们证明DKAJ在与最先进的基线模型竞争的同时，能够提供可视化来辅助模型解释。
英文摘要：
We propose an interpretable deep competing risks model called the Deep Kernel Aalen-Johansen (DKAJ) estimator, which generalizes the classical Aalen-Johansen nonparametric estimate of cumulative incidence functions (CIFs). Each data point (e.g., patient) is represented as a weighted combination of clusters. If a data point has nonzero weight only for one cluster, then its predicted CIFs correspond to those of the classical Aalen-Johansen estimator restricted to data points from that cluster. These weights come from an automatically learned kernel function that measures how similar any two data points are. On four standard competing risks datasets, we show that DKAJ is competitive with state-of-the-art baselines while being able to provide visualizations to assist model interpretation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
