FastCPH: Efficient Survival Analysis for Neural Networks
The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a linear model in its original form. In recent years, several methods have been proposed to generalize the Cox model to neural networks, but none of these are both numerically correct and computationally efficient. We propose FastCPH, a new method that runs in linear time and supports both the standard Breslow and Efron methods for tied events. We also demonstrate the performance of FastCPH combined with LassoNet, a neural network that provides interpretability through feature sparsity, on survival datasets. The final procedure is efficient, selects useful covariates and outperforms existing CoxPH approaches.
Code (2)
Tasks
Survival AnalysisSimilar Papers 제목 키워드 기반
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction Intervals
Kernel survival analysis methods predict subject-specific survival curves and times using information about which training subjects are most similar to a test subject. These most similar training subjects could serve as …
PredictionPrediction IntervalsSurvival AnalysisvalidSurvival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee
Kernel survival analysis models estimate individual survival distributions with the help of a kernel function, which measures the similarity between any two data points. Such a kernel function can be learned using deep k…
Neural Architecture SearchSurvival AnalysisReduction Techniques for Survival Analysis
In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classification task, without ignoring the specific…
Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks
Survival analysis is crucial for many medical applications, but remains challenging for modern machine learning due to limited data, censoring, and the heterogeneity of tabular covariates. While the prior-fitted paradigm…
SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
Survival analysis provides a powerful statistical framework for modeling time-to-event outcomes in the presence of censoring. However, selecting an appropriate estimator from the many specialized survival approaches ofte…
Bayesian Inference