Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting …

OBJECTIVE: Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. DESIGN: Observational cohort study. SETTING: Five hospitals, from November 2008 until January 2013. PATIENTS: Hospitalized ward patients INTERVENTIONS:: None MEASUREMENTS AND MAIN RESULTS:: Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next…


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