Toward Learning Shift-Invariant Representations for Healthcare Series Classification

Abstract

Accurate classification of healthcare time series is critical for clinical decision-making. However, existing models often struggle under real-world data shifts and lack interpretability—two key requirements for reliable medical deployment. To address these challenges, we propose SHINE, a novel end-to-end framework that learns disentangled and shift-invariant representations by modeling the generative process of multivariate healthcare signals. Specifically, SHINE first introduces a genuine data representation learning that disentangles healthcare signals into trend, seasonality, and noise components, reflecting distinct temporal dynamics of healthcare series. Then, we inject several inductive biases into each component to encourage latent representations to be invariant to data shifts and aligned with their corresponding semantic units. Extensive experiments on six healthcare benchmarks spanning ECG, EEG, and continuous glucose monitoring (CGM) domains—under a variety of simulated real-world shift scenarios—demonstrate that SHINE consistently outperforms state-of-the-art baselines, providing robust performance and clinically meaningful interpretations grounded in the estimated components.