用Transformer和新损失函数，实现无需假设的生存分析密度建模。
Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysis
- 用Transformer建模时间与非时间数据，灵活捕捉复杂分布
- 提出边际-均值-方差损失，显著提升对删失数据的敏感度
- 适合医疗、工程等需精准预测生存时间的领域
生存分析在经济、工程和医疗等领域具有重要意义，能够处理时间不变和时变数据，如客户流失、材料退化和各类医学结局。面对数据的复杂性和异质性，近年来深度学习方法被成功引入以克服传统统计方法的局限。然而，现有方法普遍存在概率密度函数（PDF）结构杂乱、删失预测敏感度低、仅适用于静态数据或依赖循环神经网络进行动态建模等问题。本文提出一种新型生存回归方法UniSurv，通过优化新颖的边际-均值-方差损失函数，并利用Transformer的灵活性，无需任何先验分布假设即可生成高质量单峰PDF，可同时处理时序与非时序数据。在多个数据集上的大量实验表明，UniSurv在删失预测方面显著优于其他方法。
原文摘要 · Abstract (English)
Survival analysis holds a crucial role across diverse disciplines, such as economics, engineering and healthcare. It empowers researchers to analyze both time-invariant and time-varying data, encompassing phenomena like customer churn, material degradation and various medical outcomes. Given the complexity and heterogeneity of such data, recent endeavors have demonstrated successful integration of deep learning methodologies to address limitations in conventional statistical approaches. However, current methods typically involve cluttered probability distribution function (PDF), have lower sensitivity in censoring prediction, only model static datasets, or only rely on recurrent neural networks for dynamic modelling. In this paper, we propose a novel survival regression method capable of producing high-quality unimodal PDFs without any prior distribution assumption, by optimizing novel Margin-Mean-Variance loss and leveraging the flexibility of Transformer to handle both temporal and non-temporal data, coined UniSurv. Extensive experiments on several datasets demonstrate that UniSurv places a significantly higher emphasis on censoring compared to other methods.
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