A Machine Learning Framework for Exploring Mortality in Developing Countries with Verbal Autopsies

This research project, backed by Unity Health Toronto and the Centre for Global Health Research (CGHR), aims to explore the use of machine learning in predicting causes of death using verbal autopsy data from low-to-middle-income countries. Verbal autopsy is a cost-effective and efficient method for documenting deaths in regions with limited resources. By employing advanced analytics and artificial intelligence, this project seeks to improve the accuracy of determining causes of death, quickly identifying disease patterns and trends, which can ultimately improve public health policies, strategies, and preparedness. The success of this research is expected to strengthen Unity Health Toronto’s reputation as an innovator in global health research, enrich epidemiological understanding, and foster collaborations with global experts. Ultimately, the findings will benefit healthcare professionals, patient care, and contribute to the resilience of Canada’s healthcare system.

Faculty Supervisor:

Frank Rudzicz

Student:

Partner:

Unity Health Toronto

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Toronto

Program: