Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast …

Abstract Objective To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury. Design Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010. Patients 50,318 adult patients undergoing major surgery. Measurements We evaluated the performance of logistic regression, generalized additive models, naïve Bayes and support vector machines for forecasting postoperative sepsis and acute kidney injury. We assessed the impact of feature reduction techniques on predictive performance. Model performance was determined using the area under the receiver operating characteristic curve, accuracy, and positive predicted value. The results were reported based on a 70/30 cross validation procedure where the data were randomly split into 70% used for training the model and the 30% for validation. Main Results The areas under the receiver operating…


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