Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision-recall curve of 63.0%. The model was externally validated in a large independent patient cohort.
@article{arxiv.1904.07990,
title = {Machine learning for early prediction of circulatory failure in the intensive care unit},
author = {Stephanie L. Hyland and Martin Faltys and Matthias Hüser and Xinrui Lyu and Thomas Gumbsch and Cristóbal Esteban and Christian Bock and Max Horn and Michael Moor and Bastian Rieck and Marc Zimmermann and Dean Bodenham and Karsten Borgwardt and Gunnar Rätsch and Tobias M. Merz},
journal= {arXiv preprint arXiv:1904.07990},
year = {2019}
}
Comments
5 main figures, 1 main table, 13 supplementary figures, 5 supplementary tables; 250ppi images