LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Prediction-driven collaborative emergency medical resource allocation with deep learning and optimization

Photo from wikipedia

Abstract This study addresses two key issues, ie, the “cold-start problem” in transmission prediction of new or rare epidemics and the collaborative allocation of emergency medical resources considering multiple objectives.… Click to show full abstract

Abstract This study addresses two key issues, ie, the “cold-start problem” in transmission prediction of new or rare epidemics and the collaborative allocation of emergency medical resources considering multiple objectives. These two issues have not yet been well addressed in data-driven emergency medical resource allocation systems. A decision support prediction-then-optimization framework combing deep learning and optimization is developed to address these two issues. Two transfer learning based convolutional neural network models are built for epidemic transmission predictions in the initial and the subsequent outbreak regions using transfer learning to deal with the “cold-start problem”. A prediction-driven collaborative emergency medical resource allocation model is built to address the issue of collaborative decisions by simultaneously considering the inter- and intra-echelon resource flows in a multi-echelon system and considering the efficiency and fairness as the objective functions. A case study of the COVID-19 pandemic shows that combining transfer learning and convolutional neural networks can improve the performances of epidemic transmission predictions, and good predictions can improve both the efficiency and fairness of emergency medical resource allocation decisions. Moreover, the computational results show that the prediction errors are asymmetrically amplified in the optimization stage, and the shortage of the resource reserve quantity mediates the asymmetrical amplification effect.

Keywords: resource allocation; resource; emergency medical; medical resource

Journal Title: Journal of the Operational Research Society
Year Published: 2022

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.