通过人工智能驱动的IoMT和云计算技术增强远程患者监控
Vijay Kumar Damera1, Ramesh Cheripelli2, Narsaiah Putta3
1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad Campus, Hyderabad, India. dameravijaykumar@gmail.com.
Scientific reports
|July 5, 2025
概括
这项研究介绍了一个人工智能驱动的远程医疗框架,使用医疗物联网 (IoMT) 进行远程医疗保健. 基于变压器的自我注意模型 (TL-SAM) 显著提高了早期诊断的疾病分类准确性.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 远程医疗远程医疗
背景情况:
- 医疗物联网 (IoMT) 正在改变远程医疗监测和实时疾病检测.
- 现有系统需要提高分析患者数据的效率和准确性,以便及时进行干预.
研究的目的:
- 开发一个新的AI驱动的远程医疗框架,集成IoMT,云计算和无线传感器网络.
- 引入和评估基于变压器的自我注意模型 (TL-SAM),以改进疾病分类.
- 为了优化模型的预测性能,使用改良的野马优化与利维飞行算法 (IWHOLFA).
主要方法:
- 实现基于变压器的自我注意模型 (TL-SAM) 来从生物传感器数据中提取特征.
- 集成IoMT设备用于实时数据收集和传输到云存储库.
- 应用改进的野马优化与强制飞行算法 (IWHOLFA) 进行超参数调整.
- 使用UCI数据集进行性能评估,并与传统的深度学习模型进行比较.
主要成果:
- 与传统模型相比,TL-SAM框架实现了更高的分类准确性 (98.62%).
- 获得了高精度 (97%),回忆 (98%) 和F1得分 (97%),证明了强大的诊断能力.
- 对IoMT数据的AI驱动分析促进了早期疾病诊断和干预.
结论:
- 拟议的AI增强的IoMT系统为智能医疗保健提供了可扩展和高效的解决方案.
- 这种方法显著改善了早期疾病检测,降低了医疗保健成本,并使及时的医疗干预成为可能.
- 该TL-SAM框架代表了远程患者监测和诊断的实质性进展.
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