Development of a predictive inpatient falls risk model using machine learning.

Abstract

The aims of this study were to create a model that detects the population at risk of falls taking into account a fall prevention variable and to know the effect on the model's performance when not considering it. Traditionally, instruments for detecting fall risk are based on risk factors, not mitigating factors. Machine learning, which allows working with a wider range of variables, could improve patient risk identification. The sample was composed of adult patients admitted to the Internal Medicine service (total, n = 22,515; training, n = 11,134; validation, n = 11,381). A retrospective cohort design was used and we applied machine learning technics. Variables were extracted from electronic medical records electronic medical records. The Two-Class Bayes Point Machine algorithm was selected. Model-A (with a 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). Fall prevention was a key variable. The model that included it detected the risk of falls better than the model without it. We created a decision-making support tool that helps nurses to identify patients at risk of falling. When it is integrated in the electronic medical records, it decreases nurses' workloads by not having to collect information manually.