“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.

Deep learning for survival analysis: a review

Simon Wiegrebe, Philipp Kopper, Raphael Sonabend et al. · February 19, 2024 · Mathematics

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.

Free PDF DOI: 10.1007/s10462-023-10681-3