用可解释神经模型分析多种风险事件的生存预测。
CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models
- 基于神经加法模型，独立建模每种风险的危险函数。
- 能可视化变量与各类风险间的非线性关系。
- 适合需要透明决策依据的医疗生存分析场景。
在医疗等场景中，患者可能面临多种互斥的事件类型，竞争风险是生存分析中的关键问题。我们提出 CRISP-NAM（Competing Risks Interpretable Survival Prediction with Neural Additive Models），一种用于竞争风险生存分析的可解释神经加法模型。该模型将神经加法架构扩展至建模因果特异性危险率，同时保持特征级可解释性。每个特征通过专用神经网络独立贡献于风险估计，从而可直观展示协变量与各类竞争风险之间的复杂非线性关系。我们在多个数据集上验证了其性能，结果表明其表现优于现有方法。
原文摘要 · Abstract (English)
Competing risks are crucial considerations in survival modelling, particularly in healthcare domains where patients may experience multiple distinct event types. We propose CRISP-NAM (Competing Risks Interpretable Survival Prediction with Neural Additive Models), an interpretable neural additive model for competing risks survival analysis which extends the neural additive architecture to model cause-specific hazards while preserving feature-level interpretability. Each feature contributes independently to risk estimation through dedicated neural networks, allowing for visualization of complex non-linear relationships between covariates and each competing risk. We demonstrate competitive performance on multiple datasets compared to existing approaches.
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