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

AIM: 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. BACKGROUND: 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. METHODS: 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. RESULTS: 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). 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 is integrated in the electronic medical records, it decreases nurses' workloads by not having to collect information manually.

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

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. OBJECTIVES: To build a model to detect pressure injury risk in intensive care unit patients and to put the model into production in a real environment. METHODS: The sample comprised adult patients admitted to an intensive care unit (N = 6694) at University Hospital of Torrevieja and University Hospital of Vinalopó. A retrospective design was used to train (n = 2508) and test (n = 1769) the model and then a prospective design was used to test the model in a real environment (n = 2417). Data mining was used to extract variables from electronic medical records and a predictive model was built with machine learning techniques. The sensitivity, specificity, area under the curve, and accuracy of the model were evaluated. RESULTS: The final model used logistic regression and incorporated 23 variables. The model had sensitivity of 0.90, specificity of 0.74, and area under the curve of 0.89 during the initial test, and thus it outperformed the Norton scale. The model performed well 1 year later in a real environment. CONCLUSIONS: The model effectively predicts risk of pressure injury. This allows nurses to focus on patients at high risk for pressure injury without increasing workload.