Deep learning for survival analysis: a review

Wiegrebe, Simon; Kopper, Philipp; Sonabend, Raphael; Bischl, Bernd; Bender, Andreas (2024): Deep learning for survival analysis: a review. Artificial Intelligence Review, 57 (3). ISSN 1573-7462

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.

• Doc-Type: Article (LMU)
• Organisational unit (Faculties): 16 Mathematics, Computer Science and Statistics > Statistics
• DFG subject classification of scientific disciplines: Natural sciences
• DFG: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 491502892