Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study.

Background: Real-time prediction of sepsis is a critical yet highly challenging task. Existing studies face 2 major limitations. First, they often rely on laboratory test results that are not readily available in real time, making timely diagnosis difficult. Second, the patient's condition evolves as a typical time series, but current methods often adopt coarse modeling strategies, with model architectures that are inefficient to train and deploy effectively. Objective: This study aimed to develop a prediction model for online sepsis detection using only easily obtainable vital signs, such as heart rate and temperature, with variable-length input sequences while maintaining high predictive performance through the multiscale temporal representation learning. Methods: We propose a deep learning model, Multi-Scale Temporal-aware Contrastive Learning (MSTCL), for efficient sepsis prediction based on the intensive care unit data derived from publicly available databases. We propose a multiscale temporal model to capture both short- and long-term dependencies in variable-length physiological time series. To enhance the robustness of our model, we used contrastive learning techniques that differentiate between positive and negative sepsis progression trajectories. Input features were limited to 6 vital signs, without reliance on laboratory tests or clinical notes. Results: The model was evaluated on more than 400 patients with and without sepsis. It achieved an area under the receiver operating characteristic curve of 88.34%, a sensitivity of 89.29%, and a specificity of 73% for predicting sepsis onset based on variable-length vital-sign histories. The normalized mean absolute error for the predicted sepsis onset was 0.11%. Conclusions: Our proposed model's low complexity and rapid inference make it suitable for deployment in real-time monitoring systems and low-resource environments. The ability to learn from variable-length historical data enhances the clinical applicability of our model. Furthermore, the methodology of temporal-aware contrastive learning offers a robust and efficient solution for online sepsis detection in diverse clinical settings.