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F分数对诊断测试和预测模型的排名与其临床实用性不一致
Melissa Assel1, Andrew Vickers2
1Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Diagnostic and prognostic research
|December 7, 2025
概括
在机器学习中常用的F分数,与临床实用性相比,不一致地对诊断测试进行排名. 像净益处这样的决策分析措施为临床决策提供了更可靠的评估.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习评估 机器学习评估
- 临床决策分析 临床决策分析
背景情况:
- 由精度和回忆得出的F分数被广泛用于评估机器学习中的诊断测试和预测模型.
- 通常,F分数使用相同的权重 (β=1),尽管有差异权重的可能性.
研究的目的:
- 为了比较F得分在评估诊断测试中的实用性与净益处,标准的临床实用性指标.
- 在癌症检测场景中评估F分数的表现,优先考虑高灵敏度.
主要方法:
- 使用癌症检测场景探索了F分数与净收益的特性.
- 评估了F分数的不同权重参数 (β) 如何影响测试排名.
- 与临床直觉和实用性相比较的排名顺序.
主要成果:
- 在F分数 (F1) 中,测试并未根据临床实用性进行一致的排名,偏好了特异性而不是灵敏性.
- 净收益提供了与临床直觉一致的排名,优先考虑灵敏度.
- 调整F分数的β参数对于在不同的流行率中基于实用性的一致排名是不切实际的.
结论:
- F分数表明诊断测试和预测模型与其临床实用性相比的排名不一致.
- 像净利这样的标准决策分析措施提供可解释的单位,允许与"测试所有负面"策略进行比较,并避免模型二分化.
- 建议在评估诊断测试和预测模型时,除了区分和校准外,还应使用净效益,以符合TRIPODAI指南.
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