A novel classification method for predicting acute hypotensive episodes in critical care

TLDR: This paper designs a novel dual--boundary classification algorithm that can identify patients at risk for AHE with nearly 95% accuracy up to 120 minutes before the episode begins and significantly outperforms existing approaches in predictive accuracy, sensitivity and specificity.

Abstract: An Acute Hypotensive Episode (AHE) is the sudden onset of a period of sustained low blood pressure and is one of the most critical conditions in Intensive Care Units (ICU). Without timely medical care, it can lead to irreversible organ damage and death. By identifying patients at risk for this complication, adequate medical intervention can save lives and improve patient outcomes.In this paper we study the problem of identifying patients at risk for AHE. We cast the problem as a supervised classification task and design a novel dual--boundary classification algorithm. Our algorithm uses only past blood pressure measurements of the patients thereby being much simpler than many existing methods that use multiple sources of data. It can also be used in online or batch mode which is advantageous in an ICU setting. Our extensive experiments on 1700 patients' records demonstrate that the algorithm significantly outperforms existing approaches in predictive accuracy, sensitivity and specificity. It can identify patients at risk for AHE with nearly 95% accuracy up to 120 minutes before the episode begins.