Optimizing Drug Supply with Machine Learning
Quantum Thinkers, a student team from the University of Sadat City in Egypt, focused on improving how hospitals manage their medicine supplies — ensuring availability without unnecessary waste.
For their Curiosity Cup 2025 project, they studied Drug Supply Optimization, exploring how data and analytics can make healthcare smarter and more efficient.
In any hospital, the balance is critical:
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Too few drugs lead to delayed treatments.
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Too many result in wasted budgets and expired stock.
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Inefficient procurement disrupts patient care.
To address these challenges, the team turned to machine learning.
Using SAS Viya for Learners and real-world data from 2012 to 2024, they trained models to predict drug demand, essentially providing hospitals with a data-driven forecast for medication needs.
They compared two models, Decision Tree and Random Forest, with the Decision Tree showing slightly better performance.
The results were impressive:
Smart data enables smarter healthcare.
Congratulations to team members Ammar Yasser, Shahd Moataz, Malak Abdelfattah, and Shahad Sola, along with their Faculty Advisor, Ahmed Tealeb.
You can view their full project as a PDF under the main headline of this article.
