Huaning Tan
ABSTRACT
In digital era, time-to-event data collected from biomedical studies and healthcare are often of high dimensionality, presenting computational challenges for traditional survival models. To make full use of these data, feature selection (FS), a data processing technique for dimensionality reduction, shows great significance. This work introduces statistical, machine learning, and deep learning FS methods for time-to-event data, mainly focusing on lasso, elastic net, adaptive lasso, adaptive elastic net, random survival forest, and XGBoost. We also describe three state-of-art FS methods – BASIL, FilterDeepHit+, and SparseDeepHit+. Then, we compare C-Index of 4 basic FS methods in experiment. Finally, we discuss future challenges and draw a conclusion.
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Index Terms
- Feature selection methods for high-dimensional biomedical time-to-event data: a review
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