小柯机器人

瑞士伯尔尼大学Tobias M. Merz、苏黎世联邦理工学院Gunnar Rätsch、Karsten Borgwardt等研究人员合作使用机器学习开发了一个能够预测重症监护室病人循环衰竭的系统。这一研究成果于2020年3月9日在线发表在《自然—医学》杂志上。

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

Author: Stephanie L. Hyland, Martin Faltys, Matthias Hser, Xinrui Lyu, Thomas Gumbsch, Cristbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rtsch, Tobias M. Merz

Issue&Volume: 2020-03-09

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.

DOI: 10.1038/s41591-020-0789-4

Source: https://www.nature.com/articles/s41591-020-0789-4

Nature Medicine：《自然—医学》，创刊于1995年。隶属于施普林格·自然出版集团，最新IF：87.241
官方网址：https://www.nature.com/nm/
投稿链接：https://mts-nmed.nature.com/cgi-bin/main.plex