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相关概念视频

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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相关实验视频

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A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia
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区块链支持的数字双胞胎系统用于脑中风预测.

Venkatesh Upadrista1, Sajid Nazir2, Huaglory Tianfield2

  • 1Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland. vupadr200@caledonian.ac.uk.

Brain informatics
|January 14, 2025
PubMed
概括

这项研究引入了一个安全的,机器学习的医疗保健数字双胞胎,在脑中风预测中达到98.28%的准确性. 该系统增强了数据安全性和可扩展性,用于预测严重的健康状况.

科学领域:

  • 数字健康数字健康
  • 机器学习 机器学习
  • 医疗信息学 医疗信息学

背景情况:

  • 数字双胞胎提供实时虚拟建模,用于健康监测 (饮食,睡眠,活动).
  • 目前的数字双胞胎应用程序在预测心脏病发作,中风和癌症等严重疾病时的准确性有限.
  • 数据安全和隐私问题阻碍了医疗保健数字双胞胎的广泛采用.

研究的目的:

  • 开发一个安全的,机器学习驱动的数字双胞胎应用程序.
  • 为了提高对严重健康状况的预测准确度.
  • 加强数据安全,并确保更广泛的医疗保健应用程序的可扩展性.

主要方法:

  • 开发了一个安全的数字双胞胎应用程序,集成机器学习和联盟区块链技术.
  • 专注于提高预测准确性,数据安全性和系统可扩展性.
  • 利用选定的数据集来评估脑中风预测的准确性.

主要成果:

  • 使用开发的应用程序预测脑中风的准确率达到了98.28%.
  • 通过集团区块链与机器学习的整合,增强数据安全性,确保数据防改.
  • 证明了应用程序能够检测和纠正数据异常,并保持强大的数据保护.
关键词:
可扩展性 可扩展性互联网的医疗东西的互联网.机器学习是机器学习.可扩展性 可扩展性安全与隐私 隐私与安全

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结论:

  • 开发的数字双胞胎应用程序显著提高了严重健康状况的预测准确度.
  • 集成的区块链技术为医疗数字双胞胎提供了强大的数据安全性和防改功能.
  • 可扩展的架构允许扩展以监测各种其他病理,最小的调整.