TY  - JOUR
PY  - 2022//
TI  - Predictive inpatient falls risk model using machine learning
JO  - Journal of Nursing Management
A1  - Ladios-Martin, Mireia
A1  - Cabañero-Martínez, Maria-José
A1  - Fernández-de-Maya, José
A1  - Ballesta-López, Francisco-Javier
A1  - Belso-Garzas, Adrián
A1  - Zamora-Aznar, Francisco-Manuel
A1  - Cabrero-Garcia, Julio
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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>
LA  - en
SN  - 0966-0429
UR  - http://dx.doi.org/10.1111/jonm.13760
ID  - ref1
ER  -