Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit | Zendy

Mireia Ladios-Martin | Zendy; José FernándezdeMaya | Zendy; Francisco-Javier Ballesta-López | Zendy; Adrián Belso-Garzas | Zendy; Manuel Mas-Asencio | Zendy; María José CabañeroMartínez | Zendy

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Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit

Author(s) -

Mireia Ladios-Martin,

José FernándezdeMaya,

Francisco-Javier Ballesta-López,

Adrián Belso-Garzas,

Manuel Mas-Asencio,

María José CabañeroMartínez

Publication year - 2020

Publication title -

american journal of critical care

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.592

H-Index - 81

eISSN - 1937-710X

pISSN - 1062-3264

DOI - 10.4037/ajcc2020237

Subject(s) - medicine , workload , logistic regression , intensive care unit , emergency medicine , population , risk assessment , retrospective cohort study , medical record , intensive care medicine , surgery , computer science , computer security , environmental health , operating system

Pressure injuries are an important problem in hospital care. Detecting the population at risk for pressure injuries is the first step in any preventive strategy. Available tools such as the Norton and Braden scales do not take into account all of the relevant risk factors. Data mining and machine learning techniques have the potential to overcome this limitation.

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