Trainee Rounds seminars: AI in Medicine
Two emerging U of T researchers will present their work on new ways that artificial intelligence can change healthcare in the future.
PhD candidate, Department of Mechanical and Industrial Engineering
"A machine learning approach to predict the number of beds that will require cleaning and staff requirements in the emergency department"
Boarding inpatients is a major contributor to emergency department (ED) crowding, which can be seriously impacted by the delay in bed cleaning at the ED. Therefore, knowing the number of beds to clean will help reduce the bed turnover time and consequently will help to reduce the ED crowding. This research applied machine learning algorithms to predict the number of beds requiring cleaning. Besides, the results from the prediction model were used in a queuing model to determine the bed cleaning staff number. The outcome from this study will enable hospitals to proactively plan their resources rather than being reactive during crisis moments.
MD/PhD candidate, Department of Computer Science and Temerty Faculty of Medicine
"Development and implementation of a machine learning tool to automate vascular catheter access detection in a pediatric critical care unit"
In the Critical Care Unit (CCU), vascular catheters are used routinely for blood sampling, pressure measurement, and drug administration. Awareness of catheter utilization is required to reduce the associated adverse outcomes (i.e.: bloodstream infections). Current means of documenting utilization are manual and subject to error. We developed a novel machine learning tool to automate catheter access detection in real-time. Our model can help augment clinician insight about catheter utilization patterns, and we are currently in the process of validating this tool at the bedside.
For more information, visit https://tcairem.utoronto.ca/event/trainee-rounds-seminars-ai-medicine