提出可解释的深度竞争风险模型，融合核函数与经典估计方法。
Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks
- 用核函数自动学习数据点间相似性，生成加权组合表示。
- 在4个标准数据集上表现媲美前沿模型，预测准确率高。
- 支持可视化解释，适合医疗风险分析等需要可解释性的场景。
我们提出一种可解释的深度竞争风险模型——深度核Aalen-Johansen（DKAJ）估计器，该方法推广了经典的非参数累积发病率函数（CIFs）估计。每个数据点（如患者）被表示为若干聚类的加权组合。若某数据点仅在一个聚类上具有非零权重，则其预测的CIFs即为该聚类内经典Aalen-Johansen估计器的结果。这些权重由自动学习的核函数决定，用于衡量任意两点间的相似性。在四个标准竞争风险数据集上，DKAJ的表现与当前最优基线相当，并能提供有助于模型解释的可视化结果。
原文摘要 · Abstract (English)
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.
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