用双专家模型提升癌症生存预测准确率
Dual Mixture-of-Experts Framework for Discrete-Time Survival Analysis
- 分两路专家：一路学患者分组特征，一路学时间变化风险
- 在乳腺癌数据集上测试集C指数最高提升0.04
- 可无缝接入现有深度学习生存分析框架
生存分析旨在建模事件发生所需时间，广泛应用于临床与生物医学研究。主要挑战在于同时捕捉患者异质性以及将风险预测适应个体特征和时间动态。本文提出一种用于离散时间生存分析的双混合专家（MoE）框架。该方法结合特征编码器MoE以实现子群体感知的表征学习，以及危险率MoE，利用患者特征和时间嵌入来捕捉时间动态。这种双MoE设计可灵活融入现有的基于深度学习的生存分析流程。在METABRIC和GBSG乳腺癌数据集上，该方法表现持续优异，测试集时间依赖C指数最高提升0.04，并在集成Consurv框架后进一步提升性能。
原文摘要 · Abstract (English)
Survival analysis is a task to model the time until an event of interest occurs, widely used in clinical and biomedical research. A key challenge is to model patient heterogeneity while also adapting risk predictions to both individual characteristics and temporal dynamics. We propose a dual mixture-of-experts (MoE) framework for discrete-time survival analysis. Our approach combines a feature-encoder MoE for subgroup-aware representation learning with a hazard MoE that leverages patient features and time embeddings to capture temporal dynamics. This dual-MoE design flexibly integrates with existing deep learning based survival pipelines. On METABRIC and GBSG breast cancer datasets, our method consistently improves performance, boosting the time-dependent C-index up to 0.04 on the test sets, and yields further gains when incorporated into the Consurv framework.
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