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

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.

Overview

This package provides a comprehensive framework for competing risks survival analysis with interpretable neural additive models. CRISP-NAM combines the predictive power of deep learning with interpretability through feature-level shape functions, making it suitable for clinical and biomedical applications where understanding feature contributions is crucial.

Key Features

• Interpretable Architecture: Neural additive models that provide feature-level interpretability through shape functions
• Competing Risks Support: Native handling of multiple competing events in survival analysis
• Comprehensive Evaluation: Nested cross-validation with robust performance metrics (AUC, Brier Score, Time-dependent C-index)
• Hyperparameter Optimization: Automated tuning using Optuna with customizable search spaces
• Rich Visualizations: Automated generation of feature importance plots and shape function visualizations
• Multiple Training Modes: Standard training, hyperparameter tuning, and nested cross-validation
• Baseline Comparisons: DeepHit implementation for benchmarking against state-of-the-art methods

Requirements

Python >=3.10

Install the package

pip install crisp-nam

Install from source

1. Clone the repository

git clone git@github.com:VectorInstitute/crisp-nam.git

2. Install

via pip

cd crisp-nam
pip install -e

via uv

cd crisp-nam
uv sync

Research details

For more details regarding the research work, please refer to datasets.md and training.md within the project repository.

Contributing

Contributions are welcome! Please open issues or submit pull requests.

Citation

If you use our package, kindly acknowledge by citing our research.

@inproceedings{ramachandram2025crispnam,
    title={CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models},
    author={Ramachandram, Dhanesh and Raval, Ananya},
    booktitle={EXPLIMED 2025 - Second Workshop on Explainable AI for the Medical Domain},
    year={2025}
}

License

This project is licensed under the MIT License.