Masked Self-Attention Dynamic Imputation Model for Physiological Indicators

Abstract

Accurate imputation of missing and outlier values in intraoperative physiological indicators can assist doctors in swiftly taking measures to prevent risks during surgery, which in turn helps improve postoperative prognosis for patients. Traditional data imputation methods have focused too much on the current data itself, neglecting the contextual information provided by surrounding data points. Addressing this issue, the imputation process for intraoperative physiological indicator data obtained through monitoring has been enhanced with a model that employs a self-attention mechanism focusing on the data context for dynamic weighting during imputation. Experimental results indicate that the proposed model achieves a MAE of 2.06%, a RMSE of 7.08%, and a MRE of 2.8%, outperforming other comparative models.