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
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Veröffentlichte Publikation
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.
Dokumententyp: | Artikel (LMU) |
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Organisationseinheit (Fakultäten): | 16 Mathematik, Informatik und Statistik > Statistik |
DFG-Fachsystematik der Wissenschaftsbereiche: | Naturwissenschaften |
Veröffentlichungsdatum: | 17. Jun 2024 10:48 |
Letzte Änderung: | 17. Jun 2024 10:48 |
URI: | https://oa-fund.ub.uni-muenchen.de/id/eprint/1331 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892 |