标题： 深度生存分析模型预测事件时间结果的介绍 显示英文标题

标题： An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes

摘要： 许多应用涉及在关键事件发生之前对持续时间的推理——也称为事件时间结果。客户何时取消订阅？昏迷患者何时醒来？被判刑的罪犯何时重犯？时间到事件的结果已在生存分析领域内被广泛研究，主要由统计学、医学和可靠性工程社区研究，在20世纪70年代和80年代已有相关教材出版。本专著旨在提供一个相对自成体系的现代生存分析入门介绍。我们专注于利用神经网络预测个体数据点的时间到事件结果。我们的目标是让读者清楚地理解基本的时间到事件预测问题是什么，它与标准回归和分类有何不同，并且如何使用关键的“设计模式”一次又一次地推导出新的时间到事件预测模型，从经典的Cox比例风险模型到现代深度学习方法，例如深度核Kaplan-Meier估计器和神经常微分方程模型。我们进一步深入探讨了基本时间到事件预测设置的两个扩展：预测多个关键事件中哪一个会首先发生以及直到这个最早事件发生的时间（竞争风险设定），以及根据随着时间增长的时间序列预测时间到事件结果（动态设定）。最后，我们讨论了诸如公平性、因果推理、可解释性和统计保证等各种主题。我们的专著附带了一个代码仓库，实现了我们在详细讨论中的每一个模型和评估指标。 显示英文摘要

摘要： Many applications involve reasoning about time durations before a critical event happens--also called time-to-event outcomes. When will a customer cancel a subscription, a coma patient wake up, or a convicted criminal reoffend? Time-to-event outcomes have been studied extensively within the field of survival analysis primarily by the statistical, medical, and reliability engineering communities, with textbooks already available in the 1970s and '80s. This monograph aims to provide a reasonably self-contained modern introduction to survival analysis. We focus on predicting time-to-event outcomes at the individual data point level with the help of neural networks. Our goal is to provide the reader with a working understanding of precisely what the basic time-to-event prediction problem is, how it differs from standard regression and classification, and how key "design patterns" have been used time after time to derive new time-to-event prediction models, from classical methods like the Cox proportional hazards model to modern deep learning approaches such as deep kernel Kaplan-Meier estimators and neural ordinary differential equation models. We further delve into two extensions of the basic time-to-event prediction setup: predicting which of several critical events will happen first along with the time until this earliest event happens (the competing risks setting), and predicting time-to-event outcomes given a time series that grows in length over time (the dynamic setting). We conclude with a discussion of a variety of topics such as fairness, causal reasoning, interpretability, and statistical guarantees. Our monograph comes with an accompanying code repository that implements every model and evaluation metric that we cover in detail.