Medicine Consumption Forecasting in Hospitals using Long Short-Term Memory Model

In recent years, medicine supply chain management has become more significant, especially after the Covid-19 pandemic. The most important issue is supply chain cost control. If the drug inventory is not properly managed, it will lead to issues such as the lack of inventory of certain drugs, provision of excess inventory, increased costs, and, finally, patient dissatisfaction.

In this study, an attempt has been made to predict and manage the pharmaceutical needs of hospitals using an efficient deep-learning algorithm. The drug consumption data for ten years of Besat General Hospital in Hamedan are extracted from the HIS database. As a case study, the accuracy of the predictive model is evaluated, especially for cefazolin. We use a deep model to analyze the medical time-series data efficiently. This model consists of a Long Short-Term Memory network, which can sufficiently recognize the change history in time-series prediction applications. The proposed model with many adjustable parameters in the deep architecture will bring good performance to overcome the complexities of the learning problem.

Using the deep learning method can increase robustness by reducing the effects of complexity and uncertainty in medical data. The average forecasting error for the proposed method is 0.043, and the measured values for RMSE, MAE, and R2  are 0.335, 0.260, and 0.851, respectively.

A comprehensive comparison between some other predictive methods and the implemented model shows the outperformance of the proposed approach. Additionally, the evaluation results indicate the efficiency of the proposed approach.