CSAI: Conditional Self-Attention Imputation for Healthcare Time-series.

Abstract

We introduce the Conditional Self-Attention Imputation (CSAI) model, a novel recurrent neural network architecture designed to address imputation challenges in multivariate time series derived from hospital electronic health records (EHRs). CSAI introduces key novelties specific to EHR data: a) attention-based hidden state initialisation to capture both long- and short-range temporal dependencies, b) domain-informed temporal decay to mimic clinical recording patterns, and c) a non-uniform masking strategy that models non-random missingness. Comprehensive evaluation across four EHR benchmark datasets demonstrates CSAI's effectiveness compared to state-of-the-art architectures in data restoration and downstream tasks. CSAI is integrated into PyPOTS, an open-source Python toolbox for partially observed time series. This work significantly advances the state of neural network imputation applied to EHRs by more closely aligning algorithmic imputation with clinical realities.