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.

通过自我监督的预训练方法，SMART（自我监督的缺失感知表示学习方法）通过详细的注意力机制对缺失信息进行编码，并通过学习潜在空间中的缺失数据表示来填补缺失值，从而提高了患者健康状况预测的泛化性和鲁棒性。在六个电子健康记录任务上进行的广泛实验验证了 SMART 的有效性，表明其优于现有方法。