  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).
