Multivariate Time-Series Representation Learning for Continuous Medical Diagnosis

Multivariate time series (MTS) data in electronic health records (EHR) pose unique challenges due to their sparsity and irregular time intervals. Existing methods often tend towards imputation or isolated encoding, resulting in suboptimal representation learning. Moreover, most existing research solely focuses on single-shot diagnosis, neglecting the importance of continuous diagnosis, particularly for critically ill patients. Continuous diagnosis provides significant opportunities for timely intervention and rational resource allocation. To address these challenges, we propose an innovative multivariate time-series representation learning for continuous medical diagnosis. Specifically, we first address sparsity issues by combining feature names and record values encoded by time. Then, we utilize a transformer variant with gated units to extract contextual features. Additionally, we introduce Time Update Block, a component that combines the strengths of long short-term memory and attention mechanisms, aimed at improving the model's ability for continuous diagnosis. Based on extensive experimental evaluations on real-world medical datasets, we demonstrate the superior performance of the proposed method.