Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor

Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor

Abstract Background Early diagnosis of acute kidney injury (AKI) is a major challenge in the intensive care unit (ICU). The AKIpredictor is a set of machine-learning-based prediction models for AKI using routinely collected patient information, and accessible online. In order to evaluate its clinica...

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Bibliographic Details
Main Authors: Marine Flechet, Stefano Falini, Claudia Bonetti, Fabian Güiza, Miet Schetz, Greet Van den Berghe, Geert Meyfroidt
Format: Article
Language:English
Published: BMC 2019-08-01
Series:Critical Care
Subjects:
Acute kidney injury
AKIpredictor
Predictive modeling
Machine learning
Online Access:http://link.springer.com/article/10.1186/s13054-019-2563-x

Internet

http://link.springer.com/article/10.1186/s13054-019-2563-x

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