Grants and Contributions:
Title:
A new framework for secure predictive healthcare delivery through federated machine learning
Agreement Number:
978160
Agreement Value:
$247,280.00
Agreement Date:
Aug 4, 2021 - Apr 30, 2025
Description:
In the last decade, significant advances in artificial intelligence (AI) and machine learning (ML) techniques has created a growing interest in leveraging these techniques to achieve predictive healthcare delivery. It is expected that AI and ML will bring about a paradigm shift in the next generation healthcare systems. Such systems will utilize AI and take advantage of inexpensive high-performance and cloud computing environments. However, ML and AI are data-hungry and data-driven technologies. This characteristic, in particular, could potentially limit the utilization of AI in healthcare systems. The goal of this Project is to address these challenges using federated learning (FL). The Project will develop a secure and scalable federated learning framework for healthcare systems. To demonstrate the application of the proposed framework for predictive healthcare, the project team will extend the precision care ML model developed in a previous project and also create a new predictive model for breast cancer detection. The proposed framework will focus on secure data discovery, data mapping and negotiation, privacy preservation while providing trusted and traceable data access, and a sharing environment for ML-based healthcare systems.
Organization:
National Research Council Canada
Expected Results:
In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.
Location:
Victoria, British Columbia, CA V8P 5C2
Reference Number:
172-2021-2022-Q2-978160
Agreement Type:
Grant
Report Type:
Grants and Contributions
Recipient Business Number:
108162470
Recipient Type:
Academia
Additional Information:
This agreement has been amended 1 time(s). The end date of this agreement has been modified by 365 days.
Amendment Date
Apr 16, 2024
Recipient's Legal Name:
University of Victoria
Federal Riding Name:
Victoria
Federal Riding Number:
59042
Program:
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Purpose:
Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.
NAICS Code:
541710
Amendments: