提出不确定性感知的多变量时间序列填补框架，提升医疗数据修复可靠性。
Impute With Confidence: A Framework for Uncertainty Aware Multivariate Time Series Imputation
- 基于模型不确定性选择性填补高置信度缺失值
- 在真实EHR数据上降低填补误差并提升死亡预测性能
- 适合医疗时序数据修复及对结果可信度敏感的应用
跨多个领域的时间序列常存在缺失值。医疗领域因传感器长时间断连而尤为突出，此时对填补值提供置信度评估至关重要。现有方法大多忽略或无法估计模型不确定性。为此，我们提出一种通用框架，量化并利用不确定性实现选择性填补：仅对模型置信度高的值进行填补，避免不可靠修复。在多个真实电子病历（EHR）数据集上的实验表明，仅填补低不确定性值不仅能减少填补误差，还能提升下游任务表现。具体地，在24小时死亡率预测任务中实现显著性能提升，验证了将不确定性纳入时序填补的实际价值。
原文摘要 · Abstract (English)
Time series data with missing values is common across many domains. Healthcare presents special challenges due to prolonged periods of sensor disconnection. In such cases, having a confidence measure for imputed values is critical. Most existing methods either overlook model uncertainty or lack mechanisms to estimate it. To address this gap, we introduce a general framework that quantifies and leverages uncertainty for selective imputation. By focusing on values the model is most confident in, highly unreliable imputations are avoided. Our experiments on multiple EHR datasets, covering diverse types of missingness, demonstrate that selectively imputing less-uncertain values not only reduces imputation errors but also improves downstream tasks. Specifically, we show performance gains in a 24-hour mortality prediction task, underscoring the practical benefit of incorporating uncertainty into time series imputation.
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