It would be desirable before a surgical procedure to have a prediction rule that could accurately estimate the probability of a patient bleeding, need for blood transfusion, and other important outcomes. Such a prediction rule would allow optimal planning, more efficient use of blood bank resources, and identification of high-risk patient cohort for specific perioperative interventions. The goal of this study is to develop an efficient and accurate algorithm that could estimate the risk of multiple outcomes simultaneously. Specifically, a heterogeneous multi-task learning method is proposed for learning outcomes such as perioperative bleeding, intraoperative RBC transfusion, ICU care, and ICU length of stay. Additional outcomes not normally predicted are incorporated in the model for transfer learning and help improve the performance of relevant outcomes. Results for predicting perioperative bleeding and need for blood transfusion for patients undergoing non-cardiac operations from an institutional transfusion datamart show that the proposed method significantly increases AUC and G-Mean by more than 6% and 5% respectively over standard single-task learning methods.