Dept. of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey; Collage of Engineering, Al-Iraqia University, Baghdad, Iraq.
Dept. of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey.
Int J Med Inform. 2024 Dec;192:105608. doi: 10.1016/j.ijmedinf.2024.105608. Epub 2024 Aug 30.
The Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT).
The study's methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed our methodologies to handle and examine data from a sample of 100,000 patients afflicted with hypertension and heart disease, employing artificial intelligence algorithms. We utilized five machine-learning algorithms to enhance the accuracy of triage.
The RTOF approach has remarkable efficacy in a simulated telemedicine environment, with a triage accuracy rate of 98%. The Random Forest algorithm exhibited superior performance compared to the other approaches under scrutiny. The performance characteristics attained were an accuracy rate of 98%, a precision rate of 99%, a sensitivity rate of 98%, and a specificity rate of 100%. The findings show a significant improvement compared to the present triage methods.
The efficiency of RTOF surpasses that of existing triage frameworks, showcasing its significant ability to enhance the quality and efficacy of telemedicine solutions. This work showcases substantial enhancements compared to existing triage approaches, while also providing a scalable approach to tackle hospital congestion and optimize resource allocation in real-time. The results of our study emphasize the capacity of RTOF to mitigate hospital overcrowding, expedite medical intervention, and enable the creation of adaptable telemedicine networks. This study highlights potential avenues for further investigation into the integration of the Internet of Medical Things (IoMT) with machine learning to develop cutting-edge medical technologies.
医疗物联网（IoMT）通过实现患者护理的远程监测和管理，彻底改变了远程医疗。然而，鉴于由多个集成式医疗设备生成的大量数据，重新生成的过程存在难以有效优先处理紧急患者信息的问题。本研究的主要目标是通过实施实时分诊优化框架（RTOF）来改进紧急患者的分诊程序，这是一种利用医疗物联网（IoMT）的各种数据的创新方法。
该研究的方法利用了各种医疗物联网（IoMT）数据，例如传感器数据和从电子病历中提取的文本。第 1 层提供传感器和文本数据，第 3 层从电子病历中导入文本数据。我们采用我们的方法来处理和检查来自 100,000 名患有高血压和心脏病的患者的样本数据，使用人工智能算法。我们使用了五种机器学习算法来提高分诊的准确性。
在模拟远程医疗环境中，RTOF 方法具有显著的效果，分诊准确率达到 98％。与其他受审查的方法相比，随机森林算法表现出了卓越的性能。所获得的性能特征是准确率为 98％，精度为 99％，敏感度为 98％，特异性为 100％。与现有分诊方法相比，这些发现显示出了显著的改善。
RTOF 的效率超过了现有的分诊框架，展示了其增强远程医疗解决方案的质量和效果的巨大能力。与现有的分诊方法相比，本工作取得了重大改进，同时还提供了一种实时解决医院拥堵和优化资源分配的可扩展方法。我们的研究结果强调了 RTOF 减轻医院拥挤，加快医疗干预并实现适应性远程医疗网络的能力。这项研究突出了将医疗物联网（IoMT）与机器学习集成以开发尖端医疗技术的潜力。
