提出MENSA模型，联合建模多重临床事件的生存分析。
MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation
- 用轨迹似然项捕捉事件间的时序关系
- 在四个数据集上优于现有先进方法
- 适合处理共现或竞争性临床事件
现有时间到事件方法多聚焦于单一事件或竞争风险场景，对多重事件情形研究较少。在医疗应用中，患者可能经历多个非互斥、半竞争性的临床事件。常见做法是为每个事件训练独立的单事件模型，但忽略了事件间的依赖与共享结构。为此，我们提出MENSA（多事件生存分析网络），一种深度学习模型，可联合学习多个事件的灵活时间到事件分布，无论事件间为竞争关系还是共现关系。此外，我们引入一种新型基于轨迹的似然项，以捕捉事件间的时序顺序。在四个多事件数据集上，MENSA的预测性能优于多种先进基线方法。源代码已公开于https://github.com/thecml/mensa。
原文摘要 · Abstract (English)
Most existing time-to-event methods focus on either single-event or competing-risks settings, leaving multi-event scenarios relatively underexplored. In many healthcare applications, for example, a patient may experience multiple clinical events, that can be non-exclusive and semi-competing. A common workaround is to train independent single-event models for such multi-event problems, but this approach fails to exploit dependencies and shared structures across events. To overcome these limitations, we propose MENSA (Multi-Event Network for Survival Analysis), a deep learning model that jointly learns flexible time-to-event distributions for multiple events, whether competing or co-occurring. In addition, we introduce a novel trajectory-based likelihood term that captures the temporal ordering between events. Across four multi-event datasets, MENSA improves predictive performance over many state-of-the-art baselines. Source code is available at https://github.com/thecml/mensa.
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