This study presents a novel approach to addressing the challenge of missing
data in multivariate time series, with a particular focus on the complexities
of healthcare data. Our Conditional Self-Attention Imputation (CSAI) model,
grounded in a transformer-based framework, introduces a conditional hidden
state initialization tailored to the intricacies of medical time series data.
This methodology diverges from traditional imputation techniques by
specifically targeting the imbalance in missing data distribution, a crucial
aspect often overlooked in healthcare datasets. By integrating advanced
knowledge embedding and a non-uniform masking strategy, CSAI adeptly adjusts to
the distinct patterns of missing data in Electronic Health Records (EHRs).

这项研究提出了一种新颖的方法来解决多元时间序列中缺失数据的挑战，特别关注医疗数据的复杂性。我们的条件自注意力填补模型（CSAI），基于变换器框架，引入了针对医学时间序列数据的条件隐藏状态初始化。此方法与传统填补技术不同之处在于专门针对医疗数据集中经常被忽视的缺失数据分布的不平衡进行调整。通过整合先进的知识嵌入和非均匀掩蔽策略，CSAI 能够灵活调整至电子病历中缺失数据的独特模式。