The influx of deep learning (DL) techniques into the field of survival
analysis in recent years, coupled with the increasing availability of
high-dimensional omics data and unstructured data like images or text, has led
to substantial methodological progress; for instance, learning from such
high-dimensional or unstructured data. Numerous modern DL-based survival
methods have been developed since the mid-2010s; however, they often address
only a small subset of scenarios in the time-to-event data setting - e.g.,
single-risk right-censored survival tasks - and neglect to incorporate more
complex (and common) settings. Partially, this is due to a lack of exchange
between experts in the respective fields.
In this work, we provide a comprehensive systematic review of DL-based
methods for time-to-event analysis, characterizing them according to both
survival- and DL-related attributes. In doing so, we hope to provide a helpful
overview to practitioners who are interested in DL techniques applicable to
their specific use case as well as to enable researchers from both fields to
identify directions for future investigation. We provide a detailed
characterization of the methods included in this review as an open-source,
interactive table: this https URL As this research
area is advancing rapidly, we encourage the research community to contribute to
keeping the information up to date.

本研究综述了近年来深度学习在生存分析中的应用，包括针对高维 omics 数据和非结构化数据等的学习，旨在对从业者提供有用的综述和帮助两个领域的研究者确定未来的方向。