Deep Learning for Survival Analysis: A Review

MCML Authors

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Andreas Bender

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

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.

article WKS+24

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Artificial Intelligence Review

57.65. Feb. 2024.

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Authors

S. Wiegrebe • P. Kopper • R. Sonabend • B. Bischl • A. Bender

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability