# Deep learning for survival analysis: a review

“Deep learning for survival analysis: a review” (2024), by Simon Wiegrebe et al. Mathematics research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Simon Wiegrebe, Philipp Kopper, Raphael Sonabend, Bernd Bischl, Andreas Bender
- Published: February 19, 2024
- Field: Mathematics
- DOI: https://doi.org/10.1007/s10462-023-10681-3
- Free PDF: https://link.springer.com/content/pdf/10.1007/s10462-023-10681-3.pdf

## Abstract

The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstructured or high-dimensional data such as images, text or omics data. In this work, we conduct a comprehensive systematic review of DL-based methods for time-to-event analysis, characterizing them according to both survival- and DL-related attributes. In summary, the reviewed methods often address only a small subset of tasks relevant to time-to-event data—e.g., single-risk right-censored data—and neglect to incorporate more complex settings. Our findings are summarized in an editable, open-source, interactive table: https://survival-org.github.io/DL4Survival . As this research area is advancing rapidly, we encourage community contribution in order to keep this database up to date.

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