Efficient healthcare supply chain: A prioritized multi-task learning approach with task-specific regularization

Abstract

Healthcare supply chain management is vital for the adequate and timely delivery of essential medical resources to healthcare facilities. In this context, a machine learning model is proposed in this paper to enhance the service of healthcare supply chains by predicting both the medical supply quantities and its timely delivery. This proposed work utilizes the multi-task learning as both of the interrelated tasks, such as the “shipped quantity” of the medical supplies and its “actual days to delivery”, simultaneously optimize their performance to improve supply chain predictions significantly. The prioritized multi-task learning with task-specific regularization provides better learning for the task “actual days to delivery” over “shipped quantity” by considering its significance in healthcare. Additionally, this task-specific regularization also prevents overfitting for the training of the model. The results and its analysis show a significant advancement of 0.3522 and 0.3531 mean absolute error and mean square error for predicting both the tasks. The proposed work outperforms the existing work in terms of mean absolute error, mean square error, root mean square error and R-squared (R2). In addition, the machine learning interpretation technique is used to assess the contribution of each feature in prediction by the proposed model.