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Isfahan Artificial Intelligence Event 2023: Drug Demand Forecasting.
Meysam Jahani1, Zahra Zojaji1, AhmadReza Montazerolghaem2
1Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
Journal of Medical Signals and Sensors
|March 3, 2025
Summary
Accurate drug demand forecasting is crucial for the pharmaceutical industry. Artificial intelligence (AI) and machine learning models, enhanced with geographic data, significantly improve prediction accuracy for better drug production and distribution.
Area of Science:
- Pharmaceutical supply chain management
- Artificial intelligence in healthcare
- Predictive analytics
Background:
- Increased drug production necessitates improved demand forecasting.
- Improper production and distribution stem from unmet future needs.
- Accurate demand forecasting is vital for pharmaceutical supply chain efficiency.
Purpose of the Study:
- To introduce the Isfahan AI competitions-2023 challenge on drug demand forecasting.
- To identify and describe successful AI-driven approaches for predicting drug demand.
- To optimize drug production and distribution through accurate demand prediction.
Main Methods:
- Utilized a dataset of drug sales from 12 pharmacies, including sales amount and purchase date.
- Competitors developed models to forecast drug demand volume with minimum error rates.
- Investigated various artificial intelligence (AI) methods for demand forecasting.
Main Results:
- Machine learning methods demonstrated significant utility in drug demand forecasting.
- Incorporating geographic features into data dimensions enhanced model accuracy.
- AI-based approaches proved effective in predicting drug demand.
Conclusions:
- Machine learning is a powerful tool for pharmaceutical demand forecasting.
- Augmenting data with geographic features improves predictive model performance.
- AI technologies offer a viable solution for optimizing drug supply chains.
