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相关概念视频

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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无监督覆盖范围采样以提高临床图表审查覆盖范围可计算的表型开发:模拟和实证研究.

Zigui Wang1, Jillian H Hurst2, Chuan Hong1

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Duke University, 2424 Erwin Road, 9023 Hock Plaza, Durham, NC, 27705, United States, +1 919-691-5011.

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概括

本研究引入了覆盖范围采样,以改善从电子健康记录 (EHR) 计算可行的表型 (CP) 的发展. 与随机抽样相比,这种方法提高了患者队列多样性和CP性能.

关键词:
欧洲人权理事会 欧洲人权理事会图表审查抽样采样可计算的表型.覆盖范围指标是覆盖范围指标.电子健康记录是电子健康记录.

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科学领域:

  • 医疗信息学 医疗信息学
  • 机器学习 机器学习
  • 临床研究 临床研究

背景情况:

  • 从电子健康记录 (EHR) 开发可计算的表型 (CP) 取决于临床医生对黄金标准标签的图表审查.
  • 患者病历的随机抽样可能无法捕捉到人口的多样性,导致偏见和表现不佳的CP,特别是对于较小的亚群体.

研究的目的:

  • 为EHR数据提出一个无监督覆盖范围采样方法.
  • 增强患者队列的多样性,并改善图表审查样本中的信息覆盖率,以改善CP发展.

主要方法:

  • 实施了无监督覆盖范围采样方法,涉及患者人群聚类和分层采样.
  • 引入了最近邻居距离度量来评估样本覆盖范围.
  • 通过模拟研究和现实世界COVID-19住院CP开发,比较覆盖率采样与随机采样.

主要成果:

  • 覆盖率采样表明患者人群覆盖范围比模拟中的随机抽样更广泛.
  • 当存在子群体时,覆盖率采样将接收器运行特征曲线 (AUC) 下的面积提高了约0.03-0.05.
  • 在现实世界的COVID-19应用中,覆盖率采样产生了更具代表性的样本,并且比随机采样改善了0.02 AUC.

结论:

  • 拟议的覆盖范围采样方法易于实施,并产生更具代表性的图表审查样本.
  • 这导致CP在子群体和整体队列中表现更好.
  • 对于CP开发,应考虑超出随机选择的替代采样策略.