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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\u2014e.g., single-risk right-censored data\u2014and neglect to incorporate more complex settings. Our findings are summarized in an editable, open-source, interactive table:<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/survival-org.github.io\/DL4Survival\">https:\/\/survival-org.github.io\/DL4Survival<\/jats:ext-link>. As this research area is advancing rapidly, we encourage community contribution in order to keep this database up to date.<\/jats:p>","DOI":"10.1007\/s10462-023-10681-3","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T12:02:28Z","timestamp":1708344148000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["Deep learning for survival analysis: a review"],"prefix":"10.1007","volume":"57","author":[{"given":"Simon","family":"Wiegrebe","sequence":"first","affiliation":[]},{"given":"Philipp","family":"Kopper","sequence":"additional","affiliation":[]},{"given":"Raphael","family":"Sonabend","sequence":"additional","affiliation":[]},{"given":"Bernd","family":"Bischl","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,19]]},"reference":[{"key":"10681_CR1","unstructured":"Aastha, Huang P, Liu Y (2021) DeepCompete: a deep learning approach to competing risks in continuous time domain. 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