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Feature Selection for Survival Analysis with Competing Risks using Deep Learning by Rietschel, Carl; Yoon, Jinsung; van der Schaar, Mihaela is a scholarly article available to read on EtoBox.

What is Feature Selection for Survival Analysis with Competing Risks using Deep Learning about?

Deep learning models for survival analysis have gained significant attention in the literature, but they suffer from severe performance deficits when the dataset contains many irrelevant features. We give empirical evidence for this problem in real-world medical settings using the state-of-the-art model DeepHit. Furthermore, we develop methods to improve the deep learning model through novel approaches to feature selection in survival analysis. We propose filter methods for hard feature selection and a neural network architecture that weights features for soft feature selection. Our experiments on two real-world medical datasets demonstrate that substantial performance improvements against the original models are achievable.

Author

Rietschel, Carl; Yoon, Jinsung; van der Schaar, Mihaela

Published

2018

Language

EN