Reinterpreting survival analysis in the universal approximator age
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Abstract (arXiv)
Survival analysis is an integral part of the statistical toolbox. However, while most domains of classical statistics have embraced deep learning, survival analysis only recently gained some minor attention from the deep learning community. This recent development is likely in part motivated by the COVID-19 pandemic. We aim to provide the tools needed to fully harness the potential of survival analysis in deep learning. On the one hand, we discuss how survival analysis connects to classification and regression. On the other hand, we provide technical tools. We provide a new loss function, evaluation metrics, and the first universal approximating network that provably produces survival curves without numeric integration. We show that the loss function and model outperform other approaches using a large numerical study.
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References · 15
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
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- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
- 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 358
- 2018How Does Batch Normalization Help Optimization?cited by 357
- 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 175
- 2017Machine Learning for Survival Analysis: A Surveycited by 112
- 2018Deep Recurrent Survival Analysiscited by 61
- 2021Survival Regression with Proper Scoring Rules and Monotonic Neural Networkscited by 42
- 2018Neural Likelihoods via Cumulative Distribution Functionscited by 25
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