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The study results indicate that textual information from EMRs could facilitate phenotyping of ischemic stroke when this information was combined with structured information. Furthermore, decomposition of this multi-class problem into binary classification tasks followed by aggregation of classification results could improve the performance.Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of machine-learning problems, and scalable methods for optimizing AUC have recently been proposed.