Background: While recent research efforts to reduce pressure ulcers in the clinical context have focused on key retrospective characteristics, little work has focused on creating real-time predictive models to prevent this avoidable hospital-acquired injury. Furthermore, existing machine learning heuristics often fail to surpass traditional statistical models or provide individual-level risk assessments with explanations for each patient. Thus, we sought to compare the predictive performance of five machine learning and traditional statistical modeling techniques to predict the occurrence of Hospital Acquired Pressure Injuries (HAPI). Methods: Electronic Medical Record (EMR) information was collected from 57,227 hospitalizations, containing 241 positive HAPI cases, acquired from Dartmouth Hitchcock Medical Center from April 2011 to December 2016. The five classifiers were trained to predict HAPI incidence and performance was assessed using the C-statistic or Area Under the Receiver Operating Curve (AUC). Results: Logistic Regression was the best modeling approach (AUC=0.91 ± 0.034). We report discordance between predictors deemed important by the machine learning models compared to traditional statistical model. We provide means to visually assess factors important to every patient's prediction, regardless of the modeling approach, through Shapley Additive Explanations. Conclusions: Machine learning models will continue to inform decision making processes but should be compared to traditional modeling approaches to ensure proper utilization. Disagreements between important predictors found by traditional and machine learning modeling approaches can potentially confuse clinicians and as such need to be reconciled. Future efforts to analyze time-stamped, prospective medical record data will be enhanced by patient-specific details. These developments represent important steps forward in developing real-time predictive models that can be integrated and readily deployed in electronic medical record systems to reduce unnecessary harm.

BackgroundMany machine learning heuristics integrate well with Electronic Medical Record (EMR) systems yet often fail to surpass traditional statistical models for biomedical applications.ObjectiveWe sought to compare predictive performances of 12 machine learning and traditional statistical techniques to predict the occurrence of Hospital Acquired Pressure Injuries (HAPI).MethodsEMR information was collected from 57,227 hospitalizations acquired from Dartmouth Hitchcock Medical Center (April 2011 to December 2016). Twelve classification algorithms, chosen based upon classic regression and recent machine learning techniques, were trained to predict HAPI incidence and performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC).ResultsLogistic regression achieved a performance (AUC = 0.91 ± 0.034) comparable to the other machine learning approaches. We report discordance between machine learning derived predictors compared to the traditional statistical model. We visually assessed important patient-specific factors through Shapley Additive Explanations.ConclusionsMachine learning models will continue to inform clinical decision-making processes but should be compared to traditional modeling approaches to ensure proper utilization. Disagreements between important predictors found by traditional and machine learning modeling approaches can potentially confuse clinicians and need to be reconciled. These developments represent important steps forward in developing real-time predictive models that can be integrated into EMR systems to reduce unnecessary harm.