Early-Warning Systems from Clinical Time-Series using Temporal Contrastive Learning

Timely detection of patient deterioration remains one of the most critical challenges in modern healthcare. Delays in recognition can lead to preventable morbidity, mortality, and increased clinical burden. Conventional early-warning scores, such as NEWS2 and MEWS, depend on hand-crafted thresholds that often fail to capture the nonlinear and multivariate dynamics of physiological signals. This study introduces a novel framework that leverages temporal contrastive learning to build an earlywarning system from freely available multivariate vital-sign time series. Using heart rate, blood pressure, oxygen saturation, and body temperature, we construct sliding windows that serve as the basis for self-supervised representation learning. A 1-D convolutional encoder is pretrained with an InfoNCE objective on augmented window pairs, enabling the model to capture temporal dependencies and patient-specific variability without requiring extensive labels. The encoder is subsequently fine-tuned to predict whether a deterioration event will occur within a 30- minute horizon. Experimental evaluation demonstrates strong performance: ROC–AUC of 0.78, Average Precision of 0.91, and a Brier score of 0.14, indicating both discriminative power and reliable probability estimates. Visualization through calibration curves, precision–recall plots, and t-SNE embeddings further highlights the model’s robustness. Notably, lead-time analysis suggests stable predictive utility up to one hour before deterioration, underscoring the clinical relevance of the approach. Overall, our findings suggest that temporal contrastive learning offers a promising path toward scalable and label-efficient earlywarning systems, lowering barriers for deployment in settings where access to large curated datasets is limited. Index Terms—Early warning, clinical time series, contrastive learning, temporal representation learning, self-supervised learning.