AI in Medicine
Authors: Laura Legat (Johannes Kepler Universität) , Erich Kobler (Johannes Kepler Universität)
Intensive care units (ICUs) provide lifesaving treatments to patients with severe medical conditions, producing large amounts of clinical time-series data that reflect patient health trajectories. Forecasting future trajectory changes helps clinicians anticipate adverse events. While prior work addresses the challenges of missing values and irregularities in clinical time-series, designing effective forecasting architectures for such data remains an open research area. At the same time, limitations of Transformer-based models are prompting a renewed interest in recurrent architectures for processing time-series. Among them, the recently proposed xLSTM demonstrates strong forecasting capabilities across several domains, yet its potential for clinical use-cases remains largely unexplored. In this work, we address this gap by extending xLSTM to forecast irregular multivariate clinical time-series with missing values. To this end, we replace the temporal and cross-channel modeling components of an established forecasting architecture with xLSTM blocks. Our models achieve competitive predictive performance compared to several baselines on a subset of MIMIC-III, highlighting xLSTM's potential as a powerful backbone for clinical time-series forecasting.
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How to Cite: Legat, L. & Kobler, E. (2026) “xLSTM for Irregular Multivariate Clinical Time-Series Forecasting”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision. 3(1).