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

• URL: http://arxiv.org/abs/2505.21360v3
• Date: Thu, 26 Jun 2025 18:49:10 GMT
• Title: CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models
• Authors: Dhanesh Ramachandram, Ananya Raval,
• Abstract summary: CRISP-NAM is an interpretable neural additive model for competing risks survival analysis.<n>Each feature contributes independently to risk estimation through dedicated neural networks.<n>We demonstrate competitive performance on multiple datasets compared to existing approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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