  Electronic health record (EHR) data has emerged as a valuable resource for
analyzing patient health status. However, the prevalence of missing data in EHR
poses significant challenges to existing methods, leading to spurious
correlations and suboptimal predictions. While various imputation techniques
have been developed to address this issue, they often obsess unnecessary
details and may introduce additional noise when making clinical predictions. To
tackle this problem, we propose SMART, a Self-Supervised Missing-Aware
RepresenTation Learning approach for patient health status prediction, which
encodes missing information via elaborated attentions and learns to impute
missing values through a novel self-supervised pre-training approach that
reconstructs missing data representations in the latent space. By adopting
missing-aware attentions and focusing on learning higher-order representations,
SMART promotes better generalization and robustness to missing data. We
validate the effectiveness of SMART through extensive experiments on six EHR
tasks, demonstrating its superiority over state-of-the-art methods.
