@article{ref1,
title="Predictive inpatient falls risk model using machine learning",
journal="Journal of Nursing Management",
year="2022",
author="Ladios-Martin, Mireia and Cabañero-Martínez, Maria-José and Fernández-de-Maya, José and Ballesta-López, Francisco-Javier and Belso-Garzas, Adrián and Zamora-Aznar, Francisco-Manuel and Cabrero-Garcia, Julio",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="AIM: To create a model that detects the population at risk of falls taking into account fall prevention variable and to know the effect on the model´s performance when not considering it. <br><br>BACKGROUND: Traditionally, instruments for detecting fall risk are based on risk factors, not mitigating factors. Machine learning (ML), which allows working with a wider range of variables, could improve patient risk identification. <br><br>METHODS: The sample was composed of adult patients admitted to the Internal Medicine service (total, n=22515; training, n=11134; validation, n=11381). A retrospective cohort design was used and we applied ML technics. Variables were extracted from electronic medical records (EMR). <br><br>RESULTS: The Two-Class Bayes Point Machine algorithm was selected. Model-A (with fall prevention variable) obtained better results than Model-B (without it) in sensitivity (0.74 vs 0.71), specificity (0.82 vs 0.74) and AUC (0.82 vs 0.78). <br><br>CONCLUSIONS: Fall prevention was a key variable. The model that included it detected the risk of falls better than the model without it. IMPLICATIONS FOR NURSING MANAGEMENT: We created a decision-making support tool that helps nurses to identify patients at risk of falling. When it´s integrated in the EMR, it decreases nurses' workloads by not having to collect information manually.<p /> <p>Language: en</p>",
language="en",
issn="0966-0429",
doi="10.1111/jonm.13760",
url="http://dx.doi.org/10.1111/jonm.13760"
}