Title: Mamba-IVP: A Denoising State-Space Initial Value Problem Framework for SOTA Clinical Time Series, Healthcare Alternative
Primary area: learning on time series and dynamical systems
Keywords: Clinical Time Series, Mask-Aware Dual-Mamba Encoder, Mamba-Hybrid Decoder, Initial Value Problem, Denoising Proof

Abstract: Missing clinical time series is a critical bottleneck in intensive care units (ICUs). In large-scale ICU electronic health record datasets such as MIMIC-IV, missing rates exceed 90% due to sensor failures, monitor degradation, and systemic outages, while aging devices inject unstable noise that makes reliable modeling nearly impossible. Existing methods remain unsafe for deployment: statistical heuristics distort missingness, deep models collapse under block-wise gaps and noise, and ODE- or diffusion-based approaches demand prohibitive computation. To overcome these limitations, we propose Mamba-IVP, a state-space generative model with a Mask-Aware Dual-Mamba Encoder (MADME) to handle block-wise missingness and a Mamba-Hybrid Decoder (MHD) to denoise continuous-time reconstructions. We validate our method through 61 experiments across two tasks: time series forcatsing and node classification. Our experiments involve 7 classic and state-of-the-art target models and 3 publicly available datasets: (1) it achieves state-of-the-art accuracy, reducing MSE by 3.0%, improving AUROC by 3.0%, and enhancing AUPRC by 3.9%; and (2) it remains robust under noise and block-wise missingness up to 12h, where other models degrade sharply.

TL;DR: Mamba-IVP is a lightweight state-space generative model that robustly imputes clinical time series under noise and missingness, achieving SOTA accuracy with superior efficiency.