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相关概念视频

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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Updated: Feb 28, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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医学中的机器学习

Rahul C Deo1

  • 1From Cardiovascular Research Institute, Department of Medicine and Institute for Human Genetics, University of California, San Francisco, and California Institute for Quantitative Biosciences, San Francisco. rahul.deo@ucsf.edu.

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|November 18, 2015
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此摘要是机器生成的。

机器学习在医疗保健方面显得有前途, 但临床影响有限. 这篇评论探讨了将机器学习纳入医疗实践的潜在应用和障碍.

关键词:
人工智能计算机预后情况危险因素统计数据

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科学领域:

  • 计算机科学
  • 医疗信息学
  • 数据科学

背景情况:

  • 在计算能力,数据可用性和算法方面的进步使机器学习 (ML) 在各个领域取得了突破.
  • 机器学习应用在各个行业迅速扩展, 包括对医疗分析的兴趣日益增长.

研究的目的:

  • 审查机器学习的潜在医疗应用.
  • 通过文献示例介绍基本的ML概念.
  • 确定阻碍ML临床应用的障碍,并提出解决方案.

主要方法:

  • 医学中的机器学习应用的文献综述.
  • 对医疗保健中的ML现有研究进行分析.
  • 识别挑战和克服它们的潜在策略.

主要成果:

  • 尽管有数据和算法,但与其他行业相比,ML对临床护理的影响有限.
  • 数以千计的论文将机器学习应用于医学数据中,

结论:

  • 机器学习在医学中的广泛临床应用存在重大障碍.
  • 解决这些障碍对于实现ML改变医疗保健实践的潜力至关重要.