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Hospital pharmaceutical stock and demand management using machine learning
This Master’s thesis project, conducted under a Mitacs Globalink research internship, aims to address persistent challenges in hospital pharmaceutical supply management—such as fluctuating medication demand, supply-chain delays, and inefficient manual forecasting—by developing an end-to-end AI-enabled solution. Using a large dataset of 6 million prescription records and 2 million stock movement entries, the student will apply machine learning techniques to forecast medication demand, optimize inventory levels, and minimize shortages and waste. The project also includes building a scalable smart dashboard for real-time visualization and decision support, as well as implementing rigorous model evaluation and optimization to ensure strong predictive performance. Through this work, the student will advance practical innovation in healthcare operations while strengthening their technical skills in machine learning, predictive analytics, and AI system design.
Faculty Supervisor:
Belkacem Chikhaoui
Student:
Partner:
Lebanese University
Discipline:
Computer science
Sector:
Health and Related Sciences and Technology; Information and Communications Technology (ICT)
University:
Université TÉLUQ
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.