Abstract:
Objective This study aims to predict the demand for coronary stents based on different deep learning models, in order to optimize the supply plan of coronary stents and improve the efficiency of supply and use.
Method Select the centralized procurement data of coronary stents in a large tertiary hospital from January 2022 to August 2024, use three deep learning models to predict the supply, evaluate the learning effect of the model based on the MSE curve, and verify the prediction results of the dataset through different evaluation indicators and evaluate the prediction error.
Result The MSE curve indicates that the Transformer model exhibits the strongest feature extraction and pattern recognition capabilities in this task, and the predicted supply evaluation index system verifies that the prediction results based on the Transformer model are close to the actual values.
Conclusion This study established a deep learning based coronary stent supply model, which effectively predicted the demand for coronary stents and provided reference for optimizing hospital stocking plans, thereby ensuring the use and supply of coronary stents.
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