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
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.
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.
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