Trainee Rounds seminars: AI in Medicine
Date: December 7, 2021 (Tuesday)
Time: 12pm to 1pm
How: Zoom meeting
Presenters: Tara Upshaw and Anton Nikouline
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Tara Upshaw
Graduate, Institute of Medical Science (2020), University of Toronto
Medical student, University of Calgary
Title of talk
Priority applications, opportunities and challenges for artificial intelligence in primary care: Results of a national deliberative dialogue
Abstract
With large volumes of rich data in electronic medical records, primary health care is primed for disruption by artificial intelligence (AI) technology. Because AI applications in primary care are still limited, there is a unique opportunity to engage key stakeholders in defining how it is used to enhance the discipline. For her Master’s Capstone research project, conducted in partnership with a social interventions research group called the Upstream Lab at St. Michael’s Hospital, Tara conducted 12 virtual deliberative dialogues with 22 primary care service users, 21 interprofessional providers, and 5 health system leaders from 7 Canadian provinces. Participants identified shared priorities for applying AI in primary care and a number of approaches to navigating anticipated challenges to health AI adoption.
Bio
Tara Upshaw holds a Master’s degree in Translational Health Research and a Diploma in Health Services and Policy Research from the University Toronto. She is passionate about health and social innovations that improve patient-centred care and advance health equity, and believes AI can achieve these aims if implemented in partnership with patients, providers, and the communities in which they work and live. She is currently a first-year MD student at the University of Calgary.
For more information
• Google Scholar
• Two LMP students selected for T-CAIREM Trainee Rounds
Anton Nikouline
Resident Physician PGY5
Department of Medicine, Division of Emergency Medicine
University of Toronto
Title of talk
Machine Learning in the Prediction of Massive Transfusion in Trauma
Abstract
Trauma currently accounts for 9.2% of all deaths worldwide and hemorrhage is the leading cause of preventable death. Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multi-organ failure and amount of products used. A number of prediction tools have been developed for the initiation of MHP using a variety of clinical, radiographic and laboratory findings. Using the National Trauma Data Bank and over 6 million patients between 2013-2018, we successfully created a machine learning model to adequately predict the need for MHP using pre-hospital and initial clinical data.
Bio
Anton Nikouline holds a Bachelor of Medical Sciences from the University of Western Ontario with a specialization in physiology, a Medical Doctorate from the University of Toronto and is currently finishing his emergency medicine residency at the University of Toronto. His clinical and research interests are in traumatically injured patients and critical care. Anton hopes to use machine learning to develop prediction models that help physicians make critical decisions during time-sensitive events and maintain equitable care for all.
For more information
• Twitter: @anikoul
• Q&A with Anton
