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相关概念视频

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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相关实验视频

Updated: Jun 26, 2026

A Precision Medicine Tool for Measurement and Monitoring of Hemoglobin S in Sickle Cell Disease Patients Receiving Transfusion Therapy
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使用机器学习模型预测血红蛋白延迟:我们可以在不同国家使用相同的预测模型吗?

Amber Meulenbeld1,2,3, Jarkko Toivonen4, Marieke Vinkenoog1

  • 1Donor Medicine Research, Sanquin Research, Amsterdam, The Netherlands.

Vox sanguinis
|April 18, 2024
PubMed
概括

献血血红蛋白 (Hb) 预测模型在不同的血液机构中是有效的. 这些模型显示一致的性能,无论培训数据如何.

关键词:
捐赠者的健康 捐赠者的健康转移出血红蛋白的转移.测量血红蛋白的测量方法预测 预测 预测 预测

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Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
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科学领域:

  • 输血药物 输血药物 输血药物
  • 在医疗保健中的预测建模.
  • 献血者管理 献血者管理

背景情况:

  • 个性化血红蛋白 (Hb) 预测模型可以减少捐赠推迟和成本.
  • 之前的研究表明,在Hb延期率高的情况下,模型性能更好.
  • 这项研究探讨了Hb推迟预测模型在不同血液采集机构的普遍性.

研究的目的:

  • 在血液机构之间共享时评估Hb推迟预测模型的性能.
  • 为了确定在一个环境中训练的模型是否在其他环境中表现良好.
  • 评估培训数据来源对模型通用性的影响.

主要方法:

  • 随机森林模型是使用来自五个国家的10,000名捐赠者的5年捐赠数据开发的.
  • 在参与的血液机构之间交换了训练有素的模型.
  • 使用精度回忆曲线下的面积 (AUPR) 量化模型性能;使用SHAP值评估变量重要性.

主要成果:

  • 精度回忆曲线 (AUPR) 下的面积在验证数据集和交换模型中从0.05到0.43不等.
  • 交换的模型无论培训数据的来源如何,都显示出相似的性能.
  • 预测变量的重要性在所有训练模型中基本一致,只有微小的变化.

结论:

  • 当应用到来自不同血液机构的验证数据集时,HB推迟预测模型的性能类似.
  • 训练数据的推迟率不会显著影响模型的概括性.
  • 血液机构似乎学习了与Hb推迟预测相关的可比关联.