一个理解标签泄漏在医疗保健机器学习中的框架
Sharon E Davis1, Michael E Matheny1,2,3,4, Suresh Balu5
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
Journal of the American Medical Informatics Association : JAMIA
|September 5, 2023
概括
在临床预测模型中避免标签泄漏需要仔细考虑,而不仅仅是"没有时间机器规则". 这项研究为开发有效和有用的机器学习模型提供了一个框架和建议.
科学领域:
- 临床预测建模临床预测建模
- 机器学习在医疗保健中的应用
背景情况:
- 标签泄漏,即结果信息污染了模型特征,是开发临床预测模型的一个已知的陷.
- 像"没有时间机器规则"这样的标准建议对于在复杂的临床环境中避免标签泄漏的细微差别是不够的.
研究的目的:
- 为评估临床预测模型中的潜在标签泄漏提供一个全面的框架.
- 为利益相关者提供关于在模型开发和实施过程中评估泄漏权衡的实际指导.
主要方法:
- 基于预测节奏,视角和适用性的框架的开发.
- 对现实世界的临床模型进行分析,以说明适当和不适当的标签泄漏.
主要成果:
- 该研究强调,避免标签泄漏需要一种细微的方法,而不仅仅是简单的时间规则.
- 现实世界的例子展示了管理标签泄漏的实际挑战和解决方案.
结论:
- 实施结构化框架有助于识别和减轻标签泄漏风险.
- 标准化的语言和评估维度可以改善统计学上有效和临床上有用的机器学习模型的开发.
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