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相关概念视频

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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利用不受监督的自由生活数据来估计心肺健康:系统性审查和元分析.

Alexios Dosis1,2, Aron Berger Syversen3, Mikolaj R Kowal1,2

  • 1Leeds Institute of Medical Research, University of Leeds, Clinical Sciences Building, St James's University Hospital, Beckett Street, Leeds, LS97LN, United Kingdom, 44 07754226212.

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概括
此摘要是机器生成的。

使用可穿戴数据估计心肺呼吸能力 (CRF) 对无法执行最大测试的人来说是有前途的. 虽然初步结果令人鼓舞,但由于数据的变化,需要更多的研究来临床使用.

关键词:
心脏呼吸系统的健康状况自由生活的数据.机器学习是机器学习.在外科手术期间的医学.可穿戴设备可以穿戴.

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科学领域:

  • 评估心肺呼吸系统的健康状况
  • 可穿戴技术是可穿戴的技术.
  • 机器学习在医疗保健中的应用

背景情况:

  • 传统的心肺呼吸能力 (CRF) 评估可能不适合脆弱的个人.
  • 使用自由生活的可穿戴数据进行CRF估计提供了潜在的更具代表性和可访问性的方法.
  • 随着时间的推移捕获的可穿戴数据可以提高CRF评估的临床可用性.

研究的目的:

  • 从自由生活的可穿戴数据系统地审查估计CRF的证据.
  • 评估为此目的开发的模型的性能和质量.
  • 评估这些新型CRF估计方法的临床实施潜力.

主要方法:

  • 在四个数据库 (MEDLINE,Embase,Scopus,arXiv) 中按照PRISMA指南进行系统的文献搜索.
  • 包括从连续自由生活可穿戴数据中开发CRF估计模型的研究;排除基于实验室的研究.
  • 使用随机效应模型和费舍尔Z转换组合性能指标的元相关性分析.

主要成果:

  • 18项涉及31,072名参与者的研究符合资格标准;平均年龄为46.9岁.
  • 在8项研究中使用了机器学习模型;聚合的元相关性估计为0.83 (95% CI 0.77-0.88).
  • 观察到高异质性 (I2=97%) 和数据分析中偏差风险,对数据处理清晰度存在担忧.

结论:

  • 从可穿戴数据中预测和测量CRF值之间存在一个有希望的初步协议.
  • 高异质性和缺乏外部验证,阻止了临床实施的最终结论.
  • 连续可穿戴数据流是一个有价值的资源,有可能显著推进CRF测量和监测.