Predicting Warfarin Sensitivity after Cardiovascular Surgery

Anticoagulation with Warfarin is indicated and required for post-operative cardiovascular patients. However, it is a high-risk medication with a narrow therapeutic range where sub-optimal dosing can lead to complications and even death. While multiple risk factors have been associated to Warfarin sensitivity, the prediction of optimal Warfarin dosing strategies remains ineffective and requires trial and error and close patient monitoring. This work proposes the use of machine learning and reinforcement learning algorithms to more accurately predict Warfarin requirements in post-operative cardiovascular patients, leading to decreased hospital stay and re-admission rates and increasing cost savings at cardiovascular surgery centers globally.

Faculty Supervisor:

Marzyeh Ghassemi;Anna Goldenberg


Julyan Keller-Baruch


Vector Institute


Computer science


Professional, scientific and technical services


University of Toronto



Current openings

Find the perfect opportunity to put your academic skills and knowledge into practice!

Find Projects