T-CAIREM is pleased to announce the 10 successful applicants for T-CAIREM's inaugural 2021 Student Trainee Rounds.
This competitive seminar series highlights innovative research at the intersection of artificial intelligence (AI) and healthcare across the University of Toronto’s graduate and professional programs. The selected researchers will discuss their project during one-hour virtual seminars with AI in healthcare leaders beginning in April 2021.
Here are the 10 researchers, along with the titles of their abstracts: • Tara Upshaw: Priority applications, opportunities and challenges for artificial intelligence in primary care: Results of a national deliberative dialogue • Sujay Nagaraj: Development and implementation of a machine learning tool to automate vascular catheter access detection in a pediatric critical care unit • Jethro Kwong: Development and external validation of an explainable machine learning model to predict risk of side-specific extraprostatic extension in men with prostate cancer • Tahera Yesmin: A machine learning approach to predict the number of beds that will require cleaning and staff requirements in the emergency department • Navpreet Kamboj: Addressing alarm fatigue by using machine learning to predict blood pressure changes after nitroglycerin dose titrations • Ryan Daniel: Machine learning to automate detection of pediatric papilledema from ocular point-of-care ultrasound • Anastasiia Razdaibiedina: Discovering gene-disease relationships with deep learning • Michael Balas: Near-human level detection of radiological predictors for intracerebral hemorrhage outcomes • Anglin Dent: Automated resolution of spatial transitions in tumour morphology using unsupervised deep learning digital pathology tools • Anton Nikouline: Machine learning in the prediction of massive transfusion in trauma.
