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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.
Cost Containment
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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机器学习和图形信号处理应用于医疗保健:一篇评论

Maria Alice Andrade Calazans1, Felipe A B S Ferreira2, Fernando A N Santos3

  • 1Centro de Tecnologia e Geociências, Universidade Federal de Pernambuco, Recife 50670-901, Brazil.

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概括

本综述探讨了机器学习应用于卫生科学中的图形信号处理. 这一新兴领域看起来很有前途,但需要提高临床解释性和新的数据集.

关键词:
深度学习是一种深度学习.图形信号处理 图形信号处理健康的健康健康的健康.机器学习是机器学习.

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科学领域:

  • 信号处理 信号处理
  • 图形理论是指图形的理论.
  • 机器学习是机器学习.
  • 卫生科学 卫生科学

背景情况:

  • 信号处理对于解释日常信号至关重要.
  • 图形理论将信号处理扩展到非欧几里德域,用于时间变化的信号.
  • 机器学习广泛应用于模式识别,包括健康科学.

研究的目的:

  • 识别和分析应用到图形信号处理在健康科学中的机器学习的文献.
  • 了解当前的现状,并确定这个新兴领域的研究缺口.

主要方法:

  • 在四个主要数据库中进行了系统的文献搜索:Science Direct,IEEE Xplore,ACM和MDPI.
  • 使用特定的搜索字符串来识别相关论文.
  • 从2015年起发表的总共45篇论文被纳入分析.

主要成果:

  • 机器学习应用于健康科学中的图形信号处理是一个新兴的研究领域,首批出版物出现在2015年.
  • 分析显示,需要提高结果的临床解释性,而不仅仅是表现指标.
  • 确定的研究缺口包括探索新型转型和创建新的公共数据集.

结论:

  • 机器学习应用于图形信号处理是健康科学中的一个不断增长的领域.
  • 未来的研究应该专注于提高临床相关性,开发新的方法和可访问的数据集.
  • 解决这些差距将进一步推进这些技术在医疗保健中的实际应用.