The Role of Machine Learning in Identification and Dissemination of High-Quality Clinical Research Articles from The Biomedical Literature

McMaster’s Health Information Research Unit has a long-established track record of identifying and sharing the highest quality, clinically relevant medical research to support healthcare professionals in staying up to date with the latest evidence so they can provide the best care to their patients. We have developed and used traditional search filters to help in this effort. These accurately return relevant articles but they also return many non-relevant, lower quality studies. This series of projects applies machine learning techniques to literature surveillance to develop and test algorithms that improve our ability to efficiently and accurately filter research studies, apply human assessments, and continuously improve how well the algorithm works. We will also test the feasibility of automatically extracting and summarizing information using machine learning. We will pilot test these new approaches to see if they work. Our long-term goal is to create short and clear summaries and knowledge graphs to be used by both healthcare professionals and patients.

Faculty Supervisor:

Alfonso Iorio

Student:

Partner:

EBSCO Health

Discipline:

Life Sciences

Sector:

Information and cultural industries; Retail trade

University:

McMaster University

Program: