SurvHive: a package to consistently access multiple survival-analysis packages

• URL: http://arxiv.org/abs/2502.02223v1
• Date: Tue, 04 Feb 2025 11:02:40 GMT
• Title: SurvHive: a package to consistently access multiple survival-analysis packages
• Authors: Giovanni Birolo, Ivan Rossi, Flavio Sartori, Cesare Rollo, Tiziana Sanavia, Piero Fariselli,
• Abstract summary: SurvHive is a Python-based framework designed to unify survival analysis methods within a coherent and interface modeled on scikit-learn.<n>SurvHive integrates classical statistical models with cutting-edge deep learning approaches, including transformer-based architectures and parametric survival models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Survival analysis, a foundational tool for modeling time-to-event data, has seen growing integration with machine learning (ML) approaches to handle the complexities of censored data and time-varying risks. Despite these advances, leveraging state-of-the-art survival models remains a challenge due to the fragmented nature of existing implementations, which lack standardized interfaces and require extensive preprocessing. We introduce SurvHive, a Python-based framework designed to unify survival analysis methods within a coherent and extensible interface modeled on scikit-learn. SurvHive integrates classical statistical models with cutting-edge deep learning approaches, including transformer-based architectures and parametric survival models. Using a consistent API, SurvHive simplifies model training, evaluation, and optimization, significantly reducing the barrier to entry for ML practitioners exploring survival analysis. The package includes enhanced support for hyper-parameter tuning, time-dependent risk evaluation metrics, and cross-validation strategies tailored to censored data. With its extensibility and focus on usability, SurvHive provides a bridge between survival analysis and the broader ML community, facilitating advancements in time-to-event modeling across domains. The SurvHive code and documentation are available freely at https://github.com/compbiomed-unito/survhive.

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