Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study
Llompart-Pou, Juan Antonio
Barea-Mendoza, Jesús Abelardo
Jiménez, José Manuel
Mayor, Dolores María
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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 objective is to create different predictive models of the mortality of critically traumatic patients using machine learning techniques.
Is part ofBMC Medical Research Methodology, 2020, vol. 20, núm. 262
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