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适用领域:一种新的基于实用性的方法,用于评估超越歧视的预测模型
Star Liu1, Shixiong Wei1, Harold P Lehmann1
1Johns Hopkins University School of Medicine, Baltimore, MD, United States.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
我们介绍了适用性区域 (ApAr),这是评估医疗保健中的机器学习模型的新方法. ApAr证明了一个模型在各种患者群体中的临床实用性,提供比传统指标更全面的评估.
科学领域:
- * 医学信息学 医学信息学
- * 机器学习评估
- *临床决策支持 *临床决策支持
背景情况:
- * 评估临床实践的机器学习模型需要评估其超出歧视权的实际实用性.
- *当前的方法往往侧重于像接收器操作特征曲线下的面积 (AUROC) 这样的指标,这些指标不能完全捕捉临床决策的复杂性.
研究的目的:
- * 引入和评估适用性领域 (ApAr),一种新的决策分析,基于实用性的方法来评估预测模型的性能.
- * 为了证明ApAr如何与现有指标相比,可以更全面地评估模型的临床价值.
主要方法:
- *开发适用性区域 (ApAr) 度量,它量化了预先概率和测试截止值的范围,预测模型提供了积极的实用性.
- *使用模拟数据集和三个已发表的医疗数据集验证ApAr.
- * 将ApAr排名与传统的AUROC指标分析进行比较.
主要成果:
- * ApAr指标在评估预测模型方面提供了额外的价值,补充了传统的AUROC分析.
- * 在糖尿病数据集示例中,ApAr排名最高的模型在AUROC排名 (23位) 中排名明显较低,突出了模型评估的差异.
- *较大的ApAr值表明预测模型的临床适用性范围更广.
结论:
- *适用性领域 (ApAr) 为评估预测模型的临床价值提供了一个优越的,基于实用性的框架.
- * ApAr帮助决策者确定模型的实用性是否与当地临床环境和患者群体保持一致.
- *这种方法有助于在医疗保健环境中更明智地采用和实施机器学习模型.
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