The SUDS Scholar will develop and apply machine learning models and algorithms to solve predictive problems in healthcare. There are a variety of ongoing projects in the lab that they may be assigned to based on their research interest. Below is a description of two ongoing projects in the lab.

a) Identifying patients at highest risk of dying on the liver transplant waitlist both across the United States and in Toronto. This project will leverage large datasets (~100K patients in the US and ~2K patients in Canada) to identify patterns in longitudinal (time-varying) clinical biomarkers that are predictive of patient mortality in order to help clinicians, patients and hospital systems make more informed choices on allocating livers. The models developed will be assessed to ensure their predictive performance is equitable across various patient subgroups.

b) GEMINI is a large scale dataset of clinical data from patients in hospitals across Ontario. Our group is building tools to create statistical guardrails to understand and assess the fairness, trustworthiness and the failure modes of predictive risk scores used in different hospitals.

The SUDS Scholar will be paired with a graduate student mentor who will work with them to provide additional guidance, support and mentorship during the summer internship.