超越准确性:用于评估算法偏差和性能的框架,应用于自动化睡眠评分
Michal Bechny1,2, Luigi Fiorillo3,4, Julia van der Meer5
1Institute of Computer Science, University of Bern, Bern, 3012, Switzerland. bechnymichal@gmail.com.
Scientific reports
|July 2, 2025
概括
人工智能 (AI) 睡眠得分算法几乎是完美的,但面临采用障碍. 一个新的框架揭示了偏见,并验证了临床使用的性能.
科学领域:
- 计算神经科学是一种计算神经科学.
- 医疗人工智能的人工智能
- 睡眠的药物 睡眠的药物
背景情况:
- 人工智能 (AI) 已经将睡眠得分算法推进到近乎理论上的性能限制.
- 临床采用受到验证,公平性和监督要求的阻碍.
- 目前的验证方法,如布兰德-阿尔特曼分析,可能会忽略受外部因素影响的偏差.
研究的目的:
- 提出一个普遍的框架来量化AI预测工具中的性能指标和偏差.
- 扩展验证方法,以分析性能和外部因素影响的错误的完整分布.
- 将这个框架应用于U-Sleep和YASA算法用于睡眠呼吸暂停风险评估.
主要方法:
- 开发了一个框架,分析外部因素如何影响预测性能和错误的整个分布.
- 扩展传统验证,超越基于平均值的指标,包括定量和偏差分析.
- 使用睡眠评分数据将框架应用于U-Sleep和YASA算法.
主要成果:
- 在U-Sleep和YASA中发现了偏差,例如与年龄相关的变化,表明数据不平衡或缺失信息.
- 证明两种算法在睡眠呼吸暂停风险评估中保持非劣势性能,尽管存在已知的偏差.
- 拟议的框架量化了整个分布中的偏见和表现,提供了比平均值指标更深入的见解.
结论:
- 该通用框架提供了对AI预测工具的更全面的验证,解决临床采用挑战.
- 人工智能睡眠评分算法在风险评估中显示出临床实用性,即使有已识别的偏差.
- 进一步的研究应侧重于解决发现的偏见,以提高AI算法可靠性和临床环境中的公平性.
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