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

Hyland, Stephanie L; Faltys, Martin; Hüser, Matthias; Lyu, Xinrui; Gumbsch, Thomas; Esteban, Cristóbal; Bock, Christian; Horn, Max; Moor, Michael; Rieck, Bastian; Zimmermann, Marc; Bodenham, Dean; Borgwardt, Karsten; Rätsch, Gunnar; Merz, Tobias M. (2020). Early prediction of circulatory failure in the intensive care unit using machine learning. Nature medicine, 26(3), pp. 364-373. Springer Nature 10.1038/s41591-020-0789-4

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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.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > Clinic of Intensive Care

UniBE Contributor:

Faltys, Martin, Merz, Tobias


600 Technology > 610 Medicine & health




Springer Nature




Mirella Aeberhard

Date Deposited:

06 Jul 2020 15:56

Last Modified:

05 Dec 2022 15:39

Publisher DOI:


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