Supervised Machine-Learning Algorithms in Real-time Prediction of Hypotensive Events

Abstract

Hypotension is common in critically ill patients. Early prediction of hypotensive events in the Intensive Care Units (ICUs) allows clinicians to pre-emptively treat the patient and avoid possible organ damage. In this study, we investigate the performance of various supervised machine-learning classification algorithms along with a real-time labeling technique to predict acute hypotensive events in the ICU. It is shown that logistic regression and SVM yield a better combination of specificity, sensitivity and positive predictive value (PPV). Logistic regression is able to predict 85% of events within 30 minutes of their onset with 81% PPV and 96% specificity, while SVM results in 96% specificity, 83% sensitivity and 82% PPV. To further reduce the false alarm rate, we propose a high-level decision-making algorithm that filters isolated false positives identified by the machine-learning algorithms. By implementing this technique, 24% of the false alarms are filtered. This saves 21 hours of medical staff time through 2,560 hours of monitoring and significantly reduces the disturbance caused by alarming monitors.