Machine learning in the operating room: focus, performance, and the medical record

This proposed study will significantly enhance our current understanding of how specific intra-operative factors can impact patient outcomes. Our proposed work will provide a proof of concept that machine learning can objectively predict a specific, high-impact post-operative complication, allowing us to move forward with scaling this work to a wide variety of surgical settings. Moreover, the use of machine learning to automatically assess high-risk points of a surgery has many implications, including the ability to direct risk mitigation efforts, work towards real-time assessment of operations, as well as standard-setting and credentialing for surgical procedures. The ability to automate the evaluation of a high-risk step of an operation in real time, and potentially change a patient’s outcome, undoubtably has the potential to significantly improve patient safety on a large scale.

Faculty Supervisor:

Frank Rudzicz


Shuja Khalid;Bonnie Armstrong;John Chen


Vector Institute



Professional, scientific and technical services


University of Toronto



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