Taking meredith out of Grey's anatomy: automating hospital ICU emergency signaling

Abstract

In this paper we propose a new algorithm based on kernel machines for automatic, instantaneous detection of emergencies occurring in a hospital Intensive Care Unit. The proposed algorithm takes as input the multitude of vital statistics that are continuously monitored for a critical patient in Intensive Care, learns the underlying pattern between the statistics that is naturally inherent for the particular patient, and instantaneously signals any deviation from this pattern. Through application to real data from a cardiac Intensive Care Unit at a hospital in a developing country, we show that it is possible to easily obtain high detection accuracy with low false alarm rates.