Using multi-modal data and self-supervised approaches for machine learning in healthcare

This research project aims to address the growing interest in predicting clinical outcomes using machine learning
(ML) approaches applied to Electronic Medical Record (EMR) data. The primary objective of this study is to
develop representations of both EMR and text data found in medical notes using current state-of-the-art ML
techniques. In particular, this research proposes to leverage self-supervised learning techniques to learn dynamic
representations. By doing so, the research aims to improve the prediction of clinical outcomes. Upon successful
completion, the machine learning models will effectively assist clinical decisions, which will benefit both the
company and the Canadian healthcare community.

Faculty Supervisor:

Bo Wang

Student:

Partner:

Signal 1 AI

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program: