[2603.00067] A Representation-Consistent Gated Recurrent Framework for Robust Medical Time-Series Classification

Computer Science > Machine Learning arXiv:2603.00067 (cs) [Submitted on 10 Feb 2026] Title:A Representation-Consistent Gated Recurrent Framework for Robust Medical Time-Series Classification Authors:Maitri Krishna Sai View a PDF of the paper titled A Representation-Consistent Gated Recurrent Framework for Robust Medical Time-Series Classification, by Maitri Krishna Sai View PDF HTML (experimental) Abstract:Medical time-series data are characterized by irregular sampling, high noise levels, missing values, and strong inter-feature dependencies. Recurrent neural networks (RNNs), particularly gated architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), are widely used for modeling such data due to their ability to capture temporal dependencies. However, standard gated recurrent models do not explicitly constrain the evolution of latent representations over time, leading to representation drift and instability under noisy or incomplete inputs. In this work, we propose a representation-consistent gated recurrent framework (RC-GRF) that introduces a principled regularization strategy to enforce temporal consistency in hidden-state representations. The proposed framework is model-agnostic and can be integrated into existing gated recurrent architectures without modifying their internal gating mechanisms. We provide a theoretical analysis demonstrating how the consistency constraint bounds hidden-state divergence and improves stability. Extensive expe...