Time-to-Event Prediction with Neural Networks and Cox Regression
By Håvard Kvamme, Ørnulf Borgan, Ida Scheel. Journal of machine learning research, 2019
466 citations recorded by the source corpus.
New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks. Building on methodology from nested case-control studies, we propose a loss function that scales well to large data sets, and enables fitting of both proportional and non-proportional extensions of the Cox model. Through simulation studies, the proposed loss function is verified to be a good approximation for the Cox partial log-likelihood. The proposed methodology is compared to existing methodologies on real-world data sets, and is found to be highly competitive, typically yielding the best performance in terms of Brier score and binomial log-likelihood. A python package for the proposed methods is available at this https URL.
Subfields: Epidemiology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c3ab842222ad344873f5aba059169d2718a9bca7
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In Statistics and Probability
Citations in the corpus
- scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn (2020, background): “Recent versions also include an experimental compatibility layer for integration with the scikit-learn API. pycox (Kvamme et al., 2019) focuses on neural networks and provides losses for proportional hazards, grouped survival time, and piecewise exponential models that can be minimized by…”