MedFlow：面向医学时间序列合成的类别感知多尺度生成方法

中文摘要 AI 辅助

合成医学时间序列的生成可缓解数据稀缺问题，助力可靠临床预测模型的开发。然而，现有方法主要聚焦于匹配真实数据的整体分布与时间动态，未必能在类别不平衡的医学数据集上保证优异的下游效用。具有临床意义的模式常出现在异质时间尺度上，而稀有的少数类特征可能被占主导的总体模式掩盖。为应对这些挑战，我们提出MedFlow，一种用于医学时间序列合成的类别感知多尺度流匹配框架。MedFlow采用矢量量化多尺度分词器，以互补的时间分辨率表示医学序列，同时捕捉粗粒度临床趋势与细粒度动态。我们进一步引入Token边际引导机制，将类别条件Token统计量直接融入流匹配过程，引导生成向学习到的Token的类别特定区域靠拢。该机制强化了少数类模式，同时保留真实数据的全局与尾部分布。在涵盖电子健康记录、脑电图（EEG）、心电图（ECG）信号的四个公开数据集上开展的实验表明，MedFlow在下游预测任务中始终优于近期基于扩散的最先进基线方法；平均而言，其AUPRC提升5.8%，Context-FID降低88.6%，采样吞吐量达到3.8倍。

英文摘要

Synthetic medical time-series generation can alleviate data scarcity and support the development of reliable clinical prediction models. However, existing methods mainly focus on matching the overall distribution and temporal dynamics of real data, which does not necessarily ensure strong downstream utility on imbalanced medical datasets. Clinically informative patterns often occur at heterogeneous temporal scales, while rare minority-class characteristics can be obscured by dominant population patterns. To address these challenges, we propose MedFlow, a class-aware multi-scale flow matching framework for medical time-series synthesis. MedFlow employs a vector-quantized multi-scale tokenizer to represent medical sequences at complementary temporal resolutions, capturing both coarse clinical trends and fine-grained dynamics. We further introduce Token Marginal Guidance, which incorporates class-conditional token statistics directly into the flow matching process to steer generation toward class-specific regions of the learned tokens. This mechanism strengthens minority-class patterns, while preserving the global and tail distributions of real data. Experiments on four public datasets covering electronic health records, EEG, and ECG signals demonstrate that MedFlow consistently outperforms recent state-of-the-art diffusion-based baselines across downstream prediction tasks. On average, it improves AUPRC by 5.8%, reduces Context-FID by 88.6%, and achieves 3.8$\times$ higher sampling throughput.

发表机构

• Shanghai Jiao Tong University（上海交通大学）
• Nanyang Technological University（南洋理工大学）
• Tsinghua University（清华大学）

机构由 AI 辅助整理，请以论文原文为准。