用乘法融合提升不规则临床时序数据的建模能力
MedFuse: Multiplicative Embedding Fusion For Irregular Clinical Time Series
- 通过乘法调制融合特征与数值嵌入，捕捉特征间动态交互
- 在三个真实医疗数据集上均超越现有最佳模型表现
- 适合需要处理异步、缺失数据的临床预测研究者
从电子健康记录（EHR）中提取的临床时序数据具有采样异步、存在缺失值及特征动态异质性的特点。尽管数值型检验指标信息丰富，现有嵌入策略多采用加法方式融合特征身份与数值嵌入，限制了对值依赖型特征交互的建模能力。本文提出MedFuse框架，核心为MuFuse（乘法嵌入融合）模块，通过乘法调制融合数值与特征嵌入，既保留特征特异性信息，又建模跨特征的高阶依赖关系。在涵盖重症与慢性护理的三个真实数据集上的实验表明，MedFuse在关键预测任务中持续优于当前最优基线。对学习表示的分析进一步显示，乘法融合提升了表达能力，并支持跨数据集预训练。这些结果确立了MedFuse作为不规则临床时序建模的通用方法。
原文摘要 · Abstract (English)
Clinical time series derived from electronic health records (EHRs) are inherently irregular, with asynchronous sampling, missing values, and heterogeneous feature dynamics. While numerical laboratory measurements are highly informative, existing embedding strategies usually combine feature identity and value embeddings through additive operations, which constrains their ability to capture value-dependent feature interactions. We propose MedFuse, a framework for irregular clinical time series centered on the MuFuse (Multiplicative Embedding Fusion) module. MuFuse fuses value and feature embeddings through multiplicative modulation, preserving feature-specific information while modeling higher-order dependencies across features. Experiments on three real-world datasets covering both intensive and chronic care show that MedFuse consistently outperforms state-of-the-art baselines on key predictive tasks. Analysis of the learned representations further demonstrates that multiplicative fusion enhances expressiveness and supports cross-dataset pretraining. These results establish MedFuse as a generalizable approach for modeling irregular clinical time series.
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