Enhancing Matching and Diversity in Health Research Projects through Textual Interpretation and Machine Learning

The project aims to improve the matching process in health research projects by leveraging machine learning and textual interpretation. It focuses on accurately connecting research projects, community organizations, and participants. The project addresses challenges such as diversity and inclusion by interpreting metrics, generating synthetic participant profiles, and utilizing NLP techniques for label association. By creating matching filters and relevance models, the project enhances the accuracy and relevance of pairings. The ultimate goal is to promote diversity, inclusivity, and the overall quality of health research outcomes.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Scikoop Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program: