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相关概念视频

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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使用AI-IoMT系统进行疾病预后的深度自动优化协作学习 (DACL) 模型.

Malarvizhi Nandagopal1, Koteeswaran Seerangan2, Tamilmani Govindaraju3

  • 1Department of CSE, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India.

Scientific reports
|May 4, 2024
PubMed
概括

这项研究引入了一个新的AI-IoMT框架,即深度自动优化协作学习 (DACL) 模型,用于使用患者数据诊断心脏病和糖尿病等慢性疾病. 该框架提高了数据准确性,并优化了特征选择,以精确识别疾病.

关键词:
人工智能 (AI) 是一种人工智能.分类.1 分类.1 分类.数据归算数据的归算方法深度自动优化协作学习 (DACL) 模型疾病的诊断 疾病的诊断医疗事物的互联网 (IoMT)优化优化 优化优化

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 医疗事物的互联网 (IoMT)

背景情况:

  • 整合人工智能 (AI) 和医疗物联网 (IoMT) 在现代医疗保健中为疾病管理提供了显著的优势.
  • 可穿戴式传感器和相互连接的网络对于实时健康监测和疾病控制至关重要.
  • 从患者医疗记录中准确识别慢性疾病仍然是一个关键的挑战.

研究的目的:

  • 开发一个先进的AI-IoMT框架,用于准确识别和分类多种慢性疾病.
  • 通过使用患者医疗记录,增强诸如心脏病,糖尿病和中风等疾病的诊断过程.
  • 创建一个强大的系统,能够处理缺失的数据和优化功能选择,以提高诊断准确度.

主要方法:

  • 开发了深度自动优化协作学习 (DACL) 模型,这是一个新的AI-IoMT框架.
  • 使用深度自动编码模型 (DAEM) 进行数据归算和预处理.
  • 应用金花搜索 (GFS) 算法来进行最佳特征选择.
  • 实施协作偏差集成GAN (ColBGaN) 模型用于疾病分类.
  • 使用水滴优化 (WDO) 技术优化分类损失函数.

主要成果:

  • 拟议的DACL框架证明了在识别慢性疾病方面的有效性和效率.
  • 综合模型 (DAEM,GFS,ColBGaN,WDO) 有助于快速准确的疾病诊断.
  • 使用基准测试数据集和标准指标的绩效评估验证了框架的功能.

结论:

  • 开发的AI-IoMT框架DACL显示了改善慢性疾病的早期检测和管理的巨大潜力.
  • 该研究强调了AI和IoMT在创建智能医疗保健解决方案方面的协同作用.
  • 进一步验证和实施DACL模型可能会提高慢性疾病护理中的患者结果.