Multi-Gradient Siamese Temporal Model for the Prediction of Clinical Events in Rapid Response Systems

Early identification of emergency situations for intensive care unit (ICU) patients is a critical component of precision medicine. We present a Multi-gradient Siamese Temporal (MG-SiameseTS) model to predict patients’ clinical status within an 8-h window and trigger an alarm when anticipating an emergent situation. It incorporates gradient extractors, contrastive learning, focal loss, and imbalanced loss to address challenges related to bias, data quality, and interpretability. Experimental results using a practical dataset collected from the rapid response team at Chonnam National University Hospital in Korea demonstrate that the MG-SiameseTS model exhibits superior predictive capabilities for clinical events compared to several state-of-the-art methods.