Early prediction of circulatory failure in the intensive care unit using machine learning
| Autor: | Bastian Rieck, Martin Faltys, Christian Bock, Stephanie L. Hyland, Cristóbal Esteban, Dean A. Bodenham, Matthias Hüser, Gunnar Rätsch, Karsten M. Borgwardt, Max Horn, Michael Moor, Marc Zimmermann, Tobias M. Merz, Xinrui Lyu, Thomas Gumbsch |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Receiver operating characteristic business.industry Computer science Process (computing) CIRCULATORY FAILURE General Medicine Machine learning computer.software_genre Intensive care unit General Biochemistry Genetics and Molecular Biology law.invention 03 medical and health sciences ALARM Identification (information) 030104 developmental biology 0302 clinical medicine law 030220 oncology & carcinogenesis Test set Artificial intelligence 610 Medicine & health business Lead (electronics) computer |
| Zdroj: | Nature Medicine Nature Medicine, 26 (3) |
| Popis: | 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. A machine-learning algorithm based on an array of demographic, physiological and clinical information is able to predict, hours in advance, circulatory failure of patients in the intensive-care unit. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|