Background: Machine learning is increasingly used for risk stratification in healthcare. Achieving accurate predictive models does not improve outcomes if they cannot be translated into efficacious intervention. Here we examine the potential utility of an automated risk-stratification and referral intervention to screen older adults for fall risk after ED visits. Objective: This study evaluated several machine learning methodologies for the creation of a risk stratification algorithm using electronic health record (EHR) data, and estimated the effects of a resultant intervention based on algorithm performance in test data. Methods: Data available at the time of ED discharge was retrospectively collected and separated into training and test datasets. Algorithms were developed to predict the outcome of return visit for fall within 6 months of an ED index visit. Models included random forests, AdaBoost, and regression-based methods. We evaluated models both by area under the receiver operating
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