Early prediction of circulatory failure in the intensive care unit using machine learning

Date 09 March 2020   Categories Featured, News

The collaborative work with the Department of Intensive Care Medicine at Bern University Hospital on the development of a method for predicting circulatory failure in patients in intensive care units has been published today.

 

Abstract  Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monitoring alarms lead to alarm fatigue. We used machine learning to develop an early-warning system that integrates measurements from multiple organ systems using a high-resolution database with 240 patient-years of data. It predicts 90% of circulatory-failure events in the test set, with 82% identified more than 2 h in advance, resulting in an area under the receiver operating characteristic curve of 0.94 and an area under the precision-recall curve of 0.63. On average, the system raises 0.05 alarms per patient and hour. The model was externally validated in an independent patient cohort. Our model provides early identification of patients at risk for circulatory failure with a much lower false-alarm rate than conventional threshold-based systems.

This research was largely funded by the Swiss National Science Foundation.

Resources

Press