可解释的人工智能-Eval:用于对医疗保健中的解释方法进行比较评估的框架
Krish Agrawal1, Radwa El Shawi2, Nada Ahmed3
1Indian Institute of Technology Indore, Indore, MP, India.
Digital health
|October 2, 2025
概括
在医疗保健领域,RuleFit和RuleMatrix提供了强大的全球解释. 这项研究为选择可靠的机器学习可解释性方法进行临床使用提供了一个框架.
科学领域:
- 医疗保健中的人工智能
- 机器学习可解释性 机器学习可解释性
- 临床决策支持系统 临床决策支持系统
背景情况:
- 机器学习 (ML) 模型在医疗保健中至关重要,但它们的采用需要对信任和合规性进行解释.
- 在现实临床环境中评估ML解释方法的有效性至关重要.
研究的目的:
- 在医疗保健中量化比较本地和全球可解释性方法.
- 评估各种解释技术对临床应用的适用性.
主要方法:
- 引入了一种结构化的方法来评估可解释性技术.
- 评估了五种本地方法 (LIME,上下文重要性,RuleFit,RuleMatrix,Anchor) 和四种全球方法 (LIME,Anchor,RuleFit,RuleMatrix).
- 通过使用多种可解释性标准,对各种医疗保健数据集和任务进行实验.
主要成果:
- 在医疗保健任务中,RuleFit和RuleMatrix展示了一致的,强大的全球解释.
- 根据数据集和评估标准,本地解释方法表现出可变的性能.
- 在解释方法中确定了忠实性,稳定性和复杂性之间的权衡.
结论:
- 开发了一个评估医疗保健中的ML解释方法的实际框架.
- 为选择可靠和透明的ML技术进行临床部署提供了指导.
- 强调系统评估对于在敏感环境中安全地实施ML的重要性.
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