设计了一个智能物联网支持的医疗保健响应框架,用于紧急情况
Sushruta Mishra1, Hrudaya Kumar Tripathy1, Himansu Das1
1School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, 751024, India.
Scientific reports
|December 19, 2025
概括
本研究介绍了一种使用智能可穿戴设备和基因算法深度神经网络 (GA-DNN) 模型的综合健康监测和交通管理系统. 它显著减少了救护车响应时间,并在紧急情况下改善了患者优先级.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 有效的应急响应依赖于及时的患者评估和干预.
- 智能可穿戴设备和人工智能模型为持续的患者监测和早期风险检测提供了潜力.
- 交通拥堵严重阻碍了救护车服务,降低了紧急护理的效率.
研究的目的:
- 为紧急救护车服务设计一个智能响应式健康模型,整合风险分析和自适应性交通管理.
- 通过实时患者监控和交通便利化,减少救护车调度和响应延迟.
- 为早期风险识别,分类和增强患者护理开发一个原型框架.
主要方法:
- 使用智能腕带 (BioTrace-G) 来收集患者的生命体征.
- 开发了一款智能手机应用程序 ("E-response"),用于存储基因算法-深度神经网络 (GA-DNN) 模型进行风险预测.
- 将风险预测模型与自适应性交通管理单元集成,以优先考虑救护车路线.
主要成果:
- 该GA-DNN模型实现了高平均风险预测准确性 (95.2%的传感器数据,94.2%的患者数量).
- 证明了较低的平均推断延迟 (57.8秒) 和最小的假负 (6.9%) 和假阳性 (13.4%).
- 综合框架优化了患者优先级,与传统方法相比,减少了救护车调度延迟,提高了救护车的速度,减少了信号停止.
结论:
- 开发的综合框架作为实时紧急响应的可行原型.
- 该系统有效地减少了响应延迟,并通过智能风险评估和交通管理提高了患者护理.
- 这种方法具有显著的潜力,可以提高紧急医疗服务的效率和有效性.
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