Frontiers in pharmacology · 2026
Comparative performance of multiple predictive models constructed for budesonide nebulized formulation requirements.
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Chen Jian, Yu Xihui, Li Ting, Shen Shanglian, Guo Runmin, He Jingcheng, Wang Zhouyan, Chen Jinjin, Zeng Weiqiang
Аннотация
BACKGROUND: Developing rational demand forecasting plans for National Centralized Volume-Based Procurement (NCVBP) pharmaceuticals is critical to ensuring patient access while avoiding drug inventory surplus, thereby facilitating refined hospital management and reducing operational costs. This study therefore compared the predictive performance of multiple models to identify an optimal approach for forecasting hospital demand for budesonide nebulized formulation.
METHODS: Using 48 months of actual utilization data (2022-2025) for budesonide nebulized formulation, a fifth-batch NCVBP product, we systematically evaluated the forecasting performance of 10 single models, 3 hybrid models, and 1 weighted ensemble model.
RESULTS: On the pediatric hospital dataset, the TBATS-XGB hybrid model achieved the best performance on the test set, yielding the lowest mean absolute percentage error (MAPE) of 12.43%, along with a root mean square error (RMSE) of 5486.0 and a mean absolute error (MAE) of 3659.8.
CONCLUSION: The TBATS-XGB hybrid model can serve as a valuable reference for NCVBP drug quantity forecasting, addressing the limitations of conventional empirical estimation methods that often fail to capture structural changes and seasonal fluctuations in demand. Moreover, because it requires only historical usage data for deployment, this approach may be highly generalizable to hospital settings with limited data infrastructure.
METHODS: Using 48 months of actual utilization data (2022-2025) for budesonide nebulized formulation, a fifth-batch NCVBP product, we systematically evaluated the forecasting performance of 10 single models, 3 hybrid models, and 1 weighted ensemble model.
RESULTS: On the pediatric hospital dataset, the TBATS-XGB hybrid model achieved the best performance on the test set, yielding the lowest mean absolute percentage error (MAPE) of 12.43%, along with a root mean square error (RMSE) of 5486.0 and a mean absolute error (MAE) of 3659.8.
CONCLUSION: The TBATS-XGB hybrid model can serve as a valuable reference for NCVBP drug quantity forecasting, addressing the limitations of conventional empirical estimation methods that often fail to capture structural changes and seasonal fluctuations in demand. Moreover, because it requires only historical usage data for deployment, this approach may be highly generalizable to hospital settings with limited data infrastructure.