Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Mathematics Department, Faculty of Science, Al-Azhar University, Naser City, Cairo 11884, Egypt.
Int J Environ Res Public Health. 2023 Mar 8;20(6):4794. doi: 10.3390/ijerph20064794.
The Emergency Departments (EDs), in hospitals located in a few important areas in Saudi Arabia, experience a heavy inflow of patients due to viral illnesses, pandemics, and even on a few special occasions events such as Hajj or Umrah, when pilgrims travel from one region to another with severe disease conditions. Apart from the EDs, it is critical to monitor the movements of patients from EDs to other wards inside the hospital or in the region. This is to track the spread of viral illnesses that require more attention. In this scenario, Machine Learning (ML) algorithms can be used to classify the data into many classes and track the target audience. The current research article presents a Machine Learning-based Medical Data Monitoring and Classification Model for the EDs of the KSA hospitals and is named MLMDMC-ED technique. The most important aim of the proposed MLMDMC-ED technique is to monitor and track the patient's visits to the EDs, the treatment given to them based on the Canadian Emergency Department Triage and Acuity Scale (CTAS), and their Length Of Stay (LOS) in the hospital, based on their treatment requirements. A patient's clinical history is crucial in terms of making decisions during health emergencies or pandemics. So, the data should be processed so that it can be classified and visualized in different formats using the ML technique. The current research work aims at extracting the textual features from the patients' data using the metaheuristic Non-Defeatable Genetic Algorithm II (NSGA II). The data, collected from the hospitals, are classified using the Graph Convolutional Network (GCN) model. Grey Wolf Optimizer (GWO) is exploited for fine-tuning the parameters to optimize the performance of the GCN model. The proposed MLMDMC-ED technique was experimentally validated on the healthcare data and the outcomes indicated the improvements of the MLMDMC-ED technique over other models with a maximum accuracy of 91.87%.
沙特阿拉伯的一些重要地区的医院急诊部（EDs）由于病毒疾病、大流行甚至在一些特殊场合，如朝觐或 Umrah 期间，当朝圣者从一个地区到另一个地区旅行时，都会出现大量的病人涌入。除了 EDs 之外，监测病人从 EDs 转移到医院内部或该地区的其他病房的情况至关重要。这是为了跟踪需要更多关注的病毒疾病的传播。在这种情况下，可以使用机器学习（ML）算法将数据分类到许多类别，并跟踪目标受众。目前的研究文章提出了一种基于机器学习的沙特阿拉伯医院 ED 医疗数据监测和分类模型，名为 MLMDMC-ED 技术。该 MLMDMC-ED 技术的最重要目标是监测和跟踪患者对 ED 的访问、根据加拿大急诊分诊和急症量表（CTAS）对他们进行的治疗以及他们在医院的逗留时间（LOS），根据他们的治疗要求。患者的临床病史对于在紧急情况或大流行期间做出决策至关重要。因此，应处理数据，以便使用 ML 技术对其进行分类并以不同格式可视化。目前的研究工作旨在使用元启发式不可战胜遗传算法 II（NSGA II）从患者数据中提取文本特征。从医院收集的数据使用图卷积网络（GCN）模型进行分类。灰狼优化器（GWO）用于微调参数以优化 GCN 模型的性能。在医疗保健数据上对所提出的 MLMDMC-ED 技术进行了实验验证，结果表明，该技术在其他模型的最大准确率为 91.87%的情况下，提高了性能。
