Ismail Leila, Waseem Muhammad Danish
Clouds and Distributed Computing and Systems (CLOUDS) Lab, School of Computing and Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Australia.
Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory, Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, UAE.
Procedia Comput Sci. 2023;220:339-347. doi: 10.1016/j.procs.2023.03.044. Epub 2023 Apr 17.
The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.
新冠疫情的爆发揭示了在医护人员和设备短缺而加剧的情况下及时干预的关键性。疼痛程度筛查是确定患者病情严重程度的第一步。自动识别状态和情绪有助于识别患者症状，以便立即采取适当行动，并提供根据患者状态量身定制的以患者为中心的医疗计划。在本文中，我们提出了一个用于在阿拉伯联合酋长国部署的疼痛程度检测框架，并使用文献中最常用的方法评估其性能。我们的结果表明，部署疼痛程度深度学习检测框架在准确识别疼痛程度方面很有前景。
