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在医疗保健中模型开发和评估的交叉验证的实际考虑和应用例子:教程教程
Drew Wilimitis1, Colin G Walsh1
1Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, United States.
JMIR AI
|June 14, 2024
概括
本教程比较了使用电子健康记录在医疗保健中的人工智能交叉验证方法. 嵌套交叉验证减少了偏差,但增加了计算成本,为研究人员提供了实际指导.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 机器学习验证验证
背景情况:
- 交叉验证对于开发和验证医疗保健中的AI至关重要.
- 现有的教程缺乏实用,对现实世界健康数据的比较示例.
- 电子健康记录 (EHR) 为模型验证带来了独特的挑战.
研究的目的:
- 提供一个实用的教程,比较各种交叉验证技术.
- 通过使用可访问的密集护理-III (MIMIC-III) 数据集的医疗信息市场来展示这些方法.
- 为使用EHR数据进行AI建模提供最佳实践和可重复的代码.
主要方法:
- 比较K折交叉验证和嵌套交叉验证.
- 适用于分类 (死亡率预测) 和回归 (停留时间预测) 任务.
- 利用MIMIC-III数据集进行现实应用.
主要成果:
- 嵌套交叉验证有效地减少了模型性能估计中的乐观偏差.
- 嵌套交叉验证引入了增加的计算复杂性.
- 对于特定的预测任务,确定了每个方法的优缺点.
结论:
- 嵌套交叉验证是建议在医疗保健中强大的AI模型验证,尽管计算需求.
- 该教程提供可重复的资源,以提高对交叉验证技术的理解和应用.
- 鼓励研究界采用这些经过验证的方法来基于EHR的AI开发.
关键词:
在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.在临床决策过程中.进行交叉验证.数据验证数据的验证.电子健康数据是什么电子医疗保健服务 电子医疗保健服务医疗保健数据 医疗保健数据模型开发模型的发展.预测建模预测建模风险检测 风险检测在教程中,教程是指教程.更多相关视频
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