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arXiv · 2505.21360

CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models

Dhanesh Ramachandram·Ananya Raval
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Abstract

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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Dhanesh Ramachandram, Ananya Raval. 2025-05-27. CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models. https://arxiv.org/abs/2505.21360

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