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Issues And Trends In Healthcare Delivery System01:29

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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.
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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相关实验视频

Updated: Jul 25, 2025

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人工智能使COVID-19检测成为可能:技术,挑战和用例

Manisha Panjeta1, Aryan Reddy2, Rushabh Shah2

  • 1Department of Computer Science and Engineering, Thapar Institute of Engineering Technology, Punjab, 147004 India.

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概括
此摘要是机器生成的。

机器学习 (ML) 和深度学习 (DL) 模型为COVID-19检测提供灵活的解决方案. 这项研究系统地审查了ML和DL方法,评估了它们的利弊和有效性,以应对流行病.

关键词:
人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.这就是Covid-19的原因.深度学习是一种深度学习.机器学习是机器学习.

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科学领域:

  • 医疗保健中的人工智能
  • 医疗信息学 医疗信息学
  • 计算生物学 计算生物学

背景情况:

  • 医疗保健系统越来越多地利用机器学习 (ML) 和深度学习 (DL) 来提高诊断和运营效率.
  • COVID-19大流行凸显了全球医疗保健基础设施的压力,以及人工智能支持过度负担的系统的潜力.
  • 人工智能技术,特别是ML和DL,显示出适应能力,对于应对流行病等不断变化的公共卫生挑战至关重要.

研究的目的:

  • 系统地审查和分析ML和DL模型在COVID-19检测中的应用.
  • 评估各种ML和DL方法的优缺点,用于识别COVID-19.
  • 提供基于关键绩效指标的不同COVID-19检测技术的比较评估.

主要方法:

  • 应用在COVID-19检测中的ML和DL模型的系统文献综述.
  • 基于可用性,可用性,准确性和成本的检测方法的比较分析.
  • 对不同检测技术的性能进行视觉表示.
  • 讨论将人工智能整合到诊断中的挑战和未来研究方向.

主要成果:

  • 基于ML和DL的各种COVID-19检测策略的全面概述.
  • 对方法进行比较评估,突出优缺点.
  • 可视化展示了各种检测技术的性能指标.
  • 确定影响人工智能在流行病检测中的实际实施的关键因素.

结论:

  • ML和DL模型为COVID-19检测提供了可行的和可适应的工具,为改善医疗保健反应提供了显著的潜力.
  • 一个系统的评估框架对于理解不同AI检测方法之间的权衡至关重要.
  • 需要进一步的研究来应对实施挑战,并优化AI与现有诊断工作流程的整合.