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

Journal: American journal of critical care : an official publication, American Association of Critical-Care Nurses
PMID: 32607572

Abstract

BACKGROUND: 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.

Authors

  • Mireia Ladios-Martin
    About the Authors: Mireia Ladios-Martin is head of quality, Ribera Salud, Valencia, Spain.
  • José Fernández-de-Maya
    José Fernández-de-Maya is a patient safety officer, University Hospital of Vinalopó, Alicante, Spain, and University Hospital of Torrevieja, Alicante, Spain.
  • Francisco-Javier Ballesta-López
    Francisco-Javier Ballesta-López is coordinator of the Population Health Management Unit, University Hospital of Vinalopó and University Hospital of Torrevieja.
  • Adrián Belso-Garzas
    Adrián Belso-Garzas is a data science lead and Manuel Mas-Asencio is a data analytics manager, Futurs, Alicante, Spain.
  • Manuel Mas-Asencio
    Adrián Belso-Garzas is a data science lead and Manuel Mas-Asencio is a data analytics manager, Futurs, Alicante, Spain.
  • María José Cabañero-Martínez
    María José Cabañero-Martínez is an associate professor, Nursing Department, University of Alicante, Spain.

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