Individualized blood transfusion management would benefit from the ability to prospectively identify patients at risk of complications of blood transfusion, and target them for closer monitoring or intervention. This study presents a simple and efficient multi-task learning method for predicting multiple surgical outcomes based on the weighted least squares support vector machine. To accelerate the training process, the input data is mapped onto a low dimensional randomized feature space leading to a simple linear system that can be solved with any existing fast linear or gradient based methods. Results for predicting early re-operation due to bleeding for patients undergoing non-cardiac operations from an institutional transfusion datamart illustrates that the method can reduce misclassification errors by as much as 13 compared to learning independent models. To further demonstrate the general applicability of the proposed method, a series of experiments are performed on synthetic data sets for scalability and on a real public data set for accuracy and robustness.