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

Main:12 Pages
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Bibliography:3 Pages
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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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@article{ramachandram2025_2505.21360,
  title={ CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models },
  author={ Dhanesh Ramachandram and Ananya Raval },
  journal={arXiv preprint arXiv:2505.21360},
  year={ 2025 }
}
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