用更灵活的专家模型提升生存预测准确率与校准度
Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads
- 设计可自适应患者特征的专家网络，突破传统分组预测限制
- 在真实医疗数据上实现更高预测精度与更低校准误差
- 适合需要高可靠性生存分析的临床研究与个性化医疗
深度混合专家模型在生存分析中备受关注，尤其因其能将相似患者聚类。但实践中聚类常导致校准误差和预测准确率下降，根源在于混合专家模型强加的归纳偏置：个体患者预测必须符合其所属群体特征。我们提出多种基于离散时间的深度混合专家架构，其中一种同时实现聚类、校准与预测准确性的优化目标。研究表明，专家表达能力是关键差异因素：能为每个患者定制预测的更灵活专家，优于依赖固定群体原型的专家。
原文摘要 · Abstract (English)
Deep mixture-of-experts models have attracted a lot of attention for survival analysis problems, particularly for their ability to cluster similar patients together. In practice, grouping often comes at the expense of key metrics such as calibration error and predictive accuracy. This is due to the restrictive inductive bias that mixture-of-experts imposes, that predictions for individual patients must look like predictions for the group they're assigned to. Might we be able to discover patient group structure, where it exists, while improving calibration and predictive accuracy? In this work, we introduce several discrete-time deep mixture-of-experts (MoE)-based architectures for survival analysis problems, one of which achieves all desiderata: clustering, calibration, and predictive accuracy. We show that a key differentiator between this array of MoEs is how expressive their experts are. We find that more expressive experts that tailor predictions per patient outperform experts that rely on fixed group prototypes.
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