GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care

Abstract

ICU (Intensive Care Unit) records comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness, yet clinical applications demand predictive models that are both accurate and interpretable. We present our Graph Attention-based Relational Learning for Intensive Care (GARLIC) model, a novel neural network architecture that imputes missing data through a learnable exponential-decay encoder, captures inter-sensor dependencies through time-lagged summary graphs, and fuses global patterns with cross-dimensional sequential attention. All attention weights and graph edges are learned end-to-end to serve as built-in observation-, signal-, and edge-level explanations. To reconcile auxiliary reconstruction and primary classification objectives, we develop an alternating decoupled optimization scheme that stabilizes training. On three ICU benchmarks (PhysioNet 2012 \& 2019, MIMIC-III) for outcome prediction, GARLIC sets the new state of the art, significantly improving AUROC and AUPRC over best-performing baselines at comparable computational cost. Ablation studies confirm each module’s contribution, and feature-removal trials validate importance attribution fidelity through a monotonic performance drop (full > top 50\% > random 50\% > bottom 50\%). Additionally, we also demonstrate GARLIC's superiority in data imputation and classification on various datasets beyond the ICU domain, showing its generalizability and applicability to broader tasks and datasets. Finally, real-time case studies demonstrate actionable risk warnings with transparent explanations, marking a significant advancement toward accurate, explainable deep learning for irregularly sampled ICU time series data.