用检索增强的稀疏采样提升医学时间序列分类的可解释性。
RAxSS: Retrieval-Augmented Sparse Sampling for Explainable Variable-Length Medical Time Series Classification
- 基于通道内相似度加权窗口预测，概率空间聚合得分。
- 在四家医院iEEG数据上达到可比性能，且输出明确证据链。
- 适合需要透明决策过程的临床时间序列分析场景。
医学时间序列分析因数据稀疏、噪声和记录长度高度可变而具挑战性。已有研究证明随机稀疏采样能有效处理变长信号，而检索增强方法可提升可解释性与对噪声及弱时序相关性的鲁棒性。本文将随机稀疏采样框架扩展为检索引导的分类方法：通过通道内相似度加权窗口预测，并在概率空间聚合，生成凸的序列级得分，同时提供明确的证据链以实现可解释性。该方法在四个医疗中心采集的iEEG数据上取得竞争力的分类表现，为临床变长时间序列分类提供了可靠且可解释的解决方案。
原文摘要 · Abstract (English)
Medical time series analysis is challenging due to data sparsity, noise, and highly variable recording lengths. Prior work has shown that stochastic sparse sampling effectively handles variable-length signals, while retrieval-augmented approaches improve explainability and robustness to noise and weak temporal correlations. In this study, we generalize the stochastic sparse sampling framework for retrieval-informed classification. Specifically, we weight window predictions by within-channel similarity and aggregate them in probability space, yielding convex series-level scores and an explicit evidence trail for explainability. Our method achieves competitive iEEG classification performance and provides practitioners with greater transparency and explainability. We evaluate our method in iEEG recordings collected in four medical centers, demonstrating its potential for reliable and explainable clinical variable-length time series classification.
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