FAIM:公平意识的可解释建模,用于医疗保健中可靠的机器学习
Mingxuan Liu1, Yilin Ning1, Yuhe Ke2
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
Patterns (New York, N.Y.)
|November 21, 2024
概括
我们开发了一个公平意识的可解释建模 (FAIM) 框架,以提高机器学习在医疗保健中的公平性. 在不牺牲性能的情况下,FAIM在医院入院预测中减少了与种族和性别有关的偏见.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 在医疗保健中整合机器学习引发了公平性问题.
- 现有的偏差缓解方法可能会损害模型性能.
研究的目的:
- 引入一个公平意识的可解释建模 (FAIM) 框架.
- 在高风险的医疗保健应用中提高模型公平性和性能.
- 减少与种族和性别有关的交叉偏见.
主要方法:
- 开发了一个具有交互界面的可解释框架 (FAIM).
- 综合数据驱动的证据与临床专业知识,以实现上下文化的公平.
- 在两个真实世界的入院数据集 (MIMIC-IV-ED,SGH-ED) 上验证了FAIM.
主要成果:
- FAIM模型显示了令人满意的歧视性性能.
- FAIM显著缓解了交叉偏见 (种族和性别).
- FAIM的表现优于常用的偏差缓解技术.
结论:
- 在不影响业绩的情况下,FAIM有效地提高了公平性.
- 该框架促进多学科合作,以实现定制的AI公平性.
- FAIM为医疗保健中公平和高性能的人工智能提供了一种实用方法.
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