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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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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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在医疗保健中运行机器学习应用的成熟度框架:范围审查

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概括
此摘要是机器生成的。

本研究探讨了医疗保健中的机器学习操作 (MLOps),提出了一个成熟度框架. 结果强调需要更好的基础设施和利益相关者参与,以推进ML在临床实践中的应用.

关键词:
ML ML 在 ML在MLOps中,MLOps是最大的.临床实践中的临床实践数据数据的数据数据的数据.我们的数据库数据库数据库数据库.医疗保健 医疗保健 医疗保健医疗保健应用程序 医疗保健应用医疗保健的实施 卫生保健的实施机器学习是机器学习.机器学习操作机器学习操作.期限框架 期限框架 期限框架 期限框架范围审查 范围审查审查

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科学领域:

  • 医疗保健信息学 医疗保健信息学
  • 机器学习工程 机器学习工程
  • 临床人工智能实施方案

背景情况:

  • 机器学习 (ML) 在医学中的应用正在迅速扩大,但在临床环境中实际实施面临重大障碍.
  • 在IT中常见的机器学习操作 (MLOps) 实践在医疗保健中研究不足,限制了ML模型的部署.
  • 现有的文献缺乏关于MLOps在医疗保健的独特背景下可行性和运行性的全面细节.

研究的目的:

  • 调查和详细介绍医疗环境中MLOps的实施情况.
  • 为医疗保健应用量身定制的新型MLOps成熟度框架提出建议.
  • 确定MLOps在医疗领域成功部署的关键组件和考虑因素.

主要方法:

  • 根据乔安娜·布里格斯研究所证据综合手册进行了范围审查.
  • 搜索了四个主要数据库 (MEDLINE,Embase,Web of Science,Scopus) 寻找有关MLOps概念验证或在医疗保健中的现实世界实施的研究.
  • 通过三阶段的基本定性内容分析,综合了19项纳入研究的结果.

主要成果:

  • 医疗保健中的MLOps工作流包括数据提取,准备,模型培训,评估,验证,部署,持续监测和持续学习.
  • 提出了一个三阶段的MLOps成熟度框架 (低,部分,完全),在19项研究中,有13项研究表明完全成熟.
  • 八项研究针对医疗保健中MLOps的关键伦理,立法和利益相关者考虑.

结论:

  • 关于在医疗保健中实施ML的研究报告有限,强调需要改进数据基础设施和协作开发.
  • 参与患者,决策者和医疗保健专业人员对于成功创建和实施ML医疗保健应用程序至关重要.
  • 研究质量的变化影响了MLOps每个工作流程步骤的分析深度,突出了当前研究的局限性.