Smart Management of Hospital Blood Bank Operations: Optimizing Units, Donors, and Stock

This Mitacs project aims to transform hospital blood bank operations into a predictive, efficient, and data-driven system using ten years of real hospital data. Traditional blood bank systems lack analytical and forecasting capabilities, leading to shortages, overstocking, and wastage. The project will develop an end-to-end AI framework that forecasts blood demand, optimizes inventory, detects shortages early, and supports decision-making through an interactive dashboard. By integrating time-series forecasting, deep learning, optimization models, anomaly detection, and rigorous model validation, the project seeks to minimize wastage, prevent stockouts, and enhance clinical reliability. Beyond improving local hospital operations, the research will generate generalizable methodologies and academic contributions that can benefit blood banks in Canada and internationally.

Faculty Supervisor:

Belkacem Chikhaoui

Student:

Partner:

Lebanese University

Discipline:

Computer science

Sector:

Health and Related Sciences and Technology; Artificial Intelligence

University:

Université TÉLUQ

Program: