使用物联网移动传感器数据进行慢性疾病监测的先进应用,机器学习算法和框架理论:系统审查
Yu Liu1, Boyuan Wang2,3
1Hefei University of Technology, Hefei, China.
Frontiers in public health
|March 10, 2025
概括
物联网 (IoT) 和人工智能 (AI) 增强了慢性疾病管理. 这些技术为诊断,风险评估和个性化治疗提供数据驱动的策略,在技术上是可行的,具有经济和社会效益.
科学领域:
- 数字健康数字健康
- 生物医学工程 生物医学工程
- 数据科学在医学中的数据科学
背景情况:
- 由于需求不断增加,传统的慢性疾病管理面临挑战.
- 物联网 (IoT) 和人工智能 (AI) 提供基于数据的解决方案.
- 移动传感设备是利用这些技术的关键.
研究的目的:
- 审查物联网移动传感器件在管理主要慢性疾病中的应用.
- 审查物联网和人工智能的有效性,包括机器学习算法,在诊断和管理中.
- 突出先进的深度学习模型和实时监控中的方法新性.
主要方法:
- 在慢性疾病管理中对物联网和人工智能的详尽文献综述.
- 对相关研究进行系统的搜索,过和质量评估.
- 分析机器学习算法 (ANN,SVM,RF,深度学习) 和基于严格框架的方法.
主要成果:
- 物联网和人工智能集成有助于风险评估,个性化治疗和疾病管理.
- 结合物联网和机器学习的平台在技术上是可行的,并提供经济/社会优势.
- 先进的深度学习模型和实时监控显示出有希望的结果.
结论:
- 物联网和人工智能技术对于精确的慢性疾病管理是有效的.
- 这些集成平台为现实世界带来了巨大的好处.
- 未来的研究方向包括量子计算和纳米技术集成.
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