针对不规则时间序列设计事件驱动掩码，提升医疗数据建模效果
EMIT- Event-Based Masked Auto Encoding for Irregular Time Series
- 基于数据变化速率选择掩码点，保留原始时间波动特征
- 在MIMIC-III与PhysioNet上显著优于传统自监督方法
- 适合医疗健康等不规则数据场景，尤其小样本下表现优异
不规则时间序列在医疗场景中广泛存在，如急诊室中生命体征和检验结果以不同间隔记录。这种时间上的不规律性反映了患者健康状态的动态变化，对临床决策至关重要。现有自监督学习方法多依赖通用预训练任务（如预测），未能充分利用不规则序列的特性。本文提出新型预训练框架EMIT，采用事件驱动的掩码机制，在隐空间进行重建，掩码点根据数据变化速率选择。该方法在保留测量时间自然波动的同时，增强模型对不规则间隔的处理能力，避免关键信息丢失。在MIMIC-III与PhysioNet Challenge数据集上的大量实验表明，事件掩码策略性能更优。代码已开源：https://github.com/hrishi-ds/EMIT。
原文摘要 · Abstract (English)
Irregular time series, where data points are recorded at uneven intervals, are prevalent in healthcare settings, such as emergency wards where vital signs and laboratory results are captured at varying times. This variability, which reflects critical fluctuations in patient health, is essential for informed clinical decision-making. Existing self-supervised learning research on irregular time series often relies on generic pretext tasks like forecasting, which may not fully utilise the signal provided by irregular time series. There is a significant need for specialised pretext tasks designed for the characteristics of irregular time series to enhance model performance and robustness, especially in scenarios with limited data availability. This paper proposes a novel pretraining framework, EMIT, an event-based masking for irregular time series. EMIT focuses on masking-based reconstruction in the latent space, selecting masking points based on the rate of change in the data. This method preserves the natural variability and timing of measurements while enhancing the model's ability to process irregular intervals without losing essential information. Extensive experiments on the MIMIC-III and PhysioNet Challenge datasets demonstrate the superior performance of our event-based masking strategy. The code has been released at https://github.com/hrishi-ds/EMIT.
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