用扩散模型生成生存分析合成数据，精准还原事件时间与删失机制。
SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis
- 基于生存分析定制的扩散模型，联合生成混合类型特征、事件时间与右删失。
- 在多个医学数据集上优于现有生成模型，兼顾分布保真与生存任务性能。
- 适合需要隐私保护数据增强的临床研究者使用。
生存分析是临床研究的核心，用于建模事件发生时间（如转移、复发或死亡）。与标准表格数据不同，生存数据常因脱落或失访导致事件信息不完整，这给合成数据生成带来独特挑战——必须真实再现事件时间分布和删失机制。本文提出SurvDiff，一种专为生存分析设计的端到端扩散模型。SurvDiff通过生存导向损失函数，联合生成混合类型协变量、事件时间与右删失，该损失函数编码时间-事件结构并直接优化下游生存任务。实验表明，SurvDiff在多个医学数据集上持续超越现有生成基线，在分布保真度和生存模型评估指标上表现更优。据我们所知，SurvDiff是首个专为生存分析合成数据设计的端到端扩散模型。
原文摘要 · Abstract (English)
Survival analysis is a cornerstone of clinical research by modeling time-to-event outcomes such as metastasis, disease relapse, or patient death. Unlike standard tabular data, survival data often come with incomplete event information due to dropout, or loss to follow-up. This poses unique challenges for synthetic data generation, where it is crucial for clinical research to faithfully reproduce both the event-time distribution and the censoring mechanism. In this paper, we propose SurvDiff an end-to-end diffusion model specifically designed for generating synthetic data in survival analysis. SurvDiff is tailored to capture the data-generating mechanism by jointly generating mixed-type covariates, event times, and right-censoring, guided by a survival-tailored loss function. The loss encodes the time-to-event structure and directly optimizes for downstream survival tasks, which ensures that SurvDiff (i) reproduces realistic event-time distributions and (ii) preserves the censoring mechanism. Across multiple datasets, we show that SurvDiff consistently outperforms state-of-the-art generative baselines in both distributional fidelity and survival model evaluation metrics across multiple medical datasets. To the best of our knowledge, SurvDiff is the first end-to-end diffusion model explicitly designed for generating synthetic survival data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。