在医疗保健时间序列 (BAHT) 中偏差分析从元数据的决策支持系统
Sagnik Dakshit1, Sristi Dakshit1, Ninad Khargonkar1
1Computer Science, The University of Texas at Dallas, Dallas, USA.
Journal of healthcare informatics research
|June 28, 2023
概括
医疗保健中的偏见人工智能阻碍了接受. 我们的框架,医疗保健时间序列中的偏差分析 (BAHT),揭示了数据集和模型中的重大偏差,影响了公平性和性能.
科学领域:
- 机器学习 机器学习
- 医疗保健人工智能的人工智能
- 公平,问责和透明度 (FAccT) ML
背景情况:
- 深度学习决策支持系统在医疗保健中越来越多地使用,但培训数据和模型中的偏差是一个主要的挑战.
- 偏见可以在现实世界部署中被放大,导致模型漂移和不公平结果等问题.
- 现有的研究往往优先考虑模型开发,而不是医疗保健中的公平性分析.
研究的目的:
- 引入一个框架来分析医疗保健时间序列数据和深度学习模型中的偏差.
- 评估突出的心电图 (ECG) 和脑电图 (EEG) 数据集中的偏差.
- 通过监督学习模型评估偏差放大,并研究偏差缓解策略.
主要方法:
- 开发了用于图形偏差分析的医疗保健时间序列偏差分析 (BAHT) 框架.
- 关于受保护变量的培训和测试数据集中的调查偏见.
- 分析了ECG和EEG数据上的监督学习模型的偏差放大.
- 实验了偏差缓解技术:采样不足,采样过多和合成数据增强.
主要成果:
- 证明了关键医疗保健时间序列数据集中普遍存在的偏见.
- 展示了现有的偏见导致潜在不公平的机器学习模型.
- 通过模型观察到显著的偏差放大,最高增幅为66.66%.
- 研究未分析偏差对模型漂移的影响.
结论:
- 医疗保健数据集中的偏见是广泛的,可以导致不公平的AI模型.
- 模型的偏差放大是一个需要解决的关键问题.
- 对数据集,模型和偏见缓解策略的适当分析对于公平的医疗保健AI交付至关重要.
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