Name
Abstract | ||
|---|---|---|
Emergency is an essential mission of public hospitals, one of its main features is to meet requirements expected by the population, whatever their nature. This work proposes a fog-based architecture integrating intelligent algorithms; based on machine learning models, to improve the emergency department performance and the patient experience. The proposed architecture effectiveness is ensured via the fog infrastructure where interactions between the smart scheduling system; deployed on the cloud, and the doctors are maintained. To ensure the efficiency property, we adopt machine learning algorithms to assign and classify patients with regards to the urgency of their cases, their waiting time and physician's availability. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/AICCSA50499.2020.9316537 | 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA) |
Keywords | DocType | ISSN |
Fog computing,Machine learning,healthcare systems,Emergy Department,Task scheduling | Conference | 2161-5322 |
ISBN | Citations | PageRank |
978-1-7281-8578-1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chafia Bouanaka | 1 | 0 | 0.68 |
| Ala Eddine Laouir | 2 | 0 | 0.34 |
| Rassim Medkour | 3 | 0 | 0.34 |