将生存分析转化为常规回归或分类任务，提升机器学习应用效率
Reduction Techniques for Survival Analysis
- 把生存分析问题转换为标准回归或分类任务
- 在多个数据集上表现优于传统生存分析方法
- 提供可直接集成到主流机器学习流程的实现方案
本文探讨了用于生存分析的缩减技术，即在不忽略生存数据特性的前提下，将生存任务转化为更常见的回归或分类任务。这类技术特别有助于基于机器学习的生存分析，使标准机器学习与深度学习工具能够广泛应用于各类生存分析任务，而无需定制专用学习器。本文综述了多种缩减技术及其优缺点，并提供了部分技术的严谨实现，使其可直接融入标准机器学习工作流。通过专用示例和基准测试，比较了这些方法与现有成熟机器学习生存分析方法的预测性能。
原文摘要 · Abstract (English)
In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classification task, without ignoring the specifics of survival data. Such techniques particularly facilitate machine learning-based survival analysis, as they allow for applying standard tools from machine and deep learning to many survival tasks without requiring custom learners. We provide an overview of different reduction techniques and discuss their respective strengths and weaknesses. We also provide a principled implementation of some of these reductions, such that they are directly available within standard machine learning workflows. We illustrate each reduction using dedicated examples and perform a benchmark analysis that compares their predictive performance to established machine learning methods for survival analysis.
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