Development of a Predictive Model for Hospital-Acquired Pressure Injuries.
| Autor: | Pouzols S; Author Affiliations: Healthcare Direction (CHUV) (Ms Pouzols and Pr Mabire); Biomedical Data Science Center (Mr Despraz and Dr Raisaro), and Institute of Higher Education and Research in Healthcare (Pr Mabire), Lausanne University Hospital; and University of Lausanne (Pr Mabire), Lausanne, Switzerland., Despraz J, Mabire C, Raisaro JL |
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| Jazyk: | angličtina |
| Zdroj: | Computers, informatics, nursing : CIN [Comput Inform Nurs] 2023 Nov 01; Vol. 41 (11), pp. 884-891. Date of Electronic Publication: 2023 Nov 01. |
| DOI: | 10.1097/CIN.0000000000001029 |
| Abstrakt: | Hospital-acquired pressure injuries are a challenge for healthcare systems, and the nurse's role is essential in their prevention. The first step is risk assessment. The development of advanced data-driven methods based on machine learning techniques can improve risk assessment through the use of routinely collected data. We studied 24 227 records from 15 937 distinct patients admitted to medical and surgical units between April 1, 2019, and March 31, 2020. Two predictive models were developed: random forest and long short-term memory neural network. Model performance was then evaluated and compared with the Braden score. The areas under the receiver operating characteristic curve, the specificity, and the accuracy of the long short-term memory neural network model (0.87, 0.82, and 0.82, respectively) were higher than those of the random forest model (0.80, 0.72, and 0.72, respectively) and the Braden score (0.72, 0.61, and 0.61, respectively). The sensitivity of the Braden score (0.88) was higher than that of long short-term memory neural network model (0.74) and the random forest model (0.73). The long short-term memory neural network model has the potential to support nurses in clinical decision-making. Implementation of this model in the electronic health record could improve assessment and allow nurses to focus on higher-priority interventions. (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.) |
| Databáze: | MEDLINE |
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