Communication Dans Un Congrès Année : 2024

Clustering Analysis for Forecasting Medicine Consumption

Résumé

This study investigates machine learning for forecasting medicine consumption in hospitals to optimize resource allocation and logistics. We use two approaches: a unified approach that combines data from multiple hospitals and a separated approach that forecasts for individual hospitals. We explored both K-means clustering and manual pair clustering based on consumption trends. While K-means clustering did not yield improvements, manual clusturing identified specific pairs of medicines with significantly enhanced forecast accuracy (e.g., Medicine 15 at Hospital 1: MAPE decreased from 19.70% to 3.30%). However, the unified approach did not consistently benefit all hospitals (e.g., Medicine 9). This underscores the need to balance accuracy gains in some hospitals against potential losses in others. Overall, manual clustering within the separated approach shows promise. Future work should explore advanced automated clustering techniques like Dynamic Time Warping (DTW) and leverage larger datasets for further validation.

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hal-04922215 , version 1 (30-01-2025)

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  • HAL Id : hal-04922215 , version 1

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Douglas Mateus Machado, Zakaria Yahouni, Gülgün Alpan, Denis Koala. Clustering Analysis for Forecasting Medicine Consumption. 5th IFAC/INSTICC International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL), Nov 2024, Porto, Portugal. ⟨hal-04922215⟩
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