Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

Abstract Background Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with respect to the affected body areas. Our objec...

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Bibliographic Details
Main Authors: Luis Serviá, Neus Montserrat, Mariona Badia, Juan Antonio Llompart-Pou, Jesús Abelardo Barea-Mendoza, Mario Chico-Fernández, Marcelino Sánchez-Casado, José Manuel Jiménez, Dolores María Mayor, Javier Trujillano
Format: Article
Language:English
Published: BMC 2020-10-01
Series:BMC Medical Research Methodology
Subjects:
Intensive care unit
Machine learning techniques
Supervised algorithms
Traumatic patient
Mortality
Online Access:http://link.springer.com/article/10.1186/s12874-020-01151-3

Internet

http://link.springer.com/article/10.1186/s12874-020-01151-3

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