用深度强化学习进行临床机器学习的算法公平性和偏差缓解.
Jenny Yang1, Andrew A S Soltan2,3, David W Eyre4
1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Nature machine intelligence
|August 24, 2023
概括
本研究介绍了一种机器学习框架,以减少医疗保健人工智能的偏见. 强化学习模型有效地预测COVID-19,同时改善不同患者群体和医院的公平性.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于临床应用.
- 卫生公平与偏见缓解
背景情况:
- 医疗保健中的机器学习模型有可能使数据偏差永久化.
- 在人工智能驱动的医疗保健中确保公平和公平是至关重要的.
- 现有的方法很难解决数据收集中的偏差问题.
研究的目的:
- 开发一种强化学习框架,以减轻医疗保健机器学习模型中的偏差.
- 评估模型在预测COVID-19和提高公平性方面的有效性.
- 在不同的医疗保健环境和任务中证明可通用性.
主要方法:
- 实施了强化学习框架,具有专门的奖励功能和培训程序.
- 评估了在急诊室患者中预测COVID-19的模型.
- 评估了医院特定和基于种族的偏见的缓解.
- 在三个独立医院进行了外部验证.
主要成果:
- 该模型实现了临床有效的COVID-19查性能.
- 与基准和最先进的方法相比,显著提高了结果的公平性.
- 在患者重症监护病房出院状态任务上证明了概括性.
结论:
- 拟议的强化学习框架有效地减轻了医疗保健AI中的偏见.
- 该方法为医学中更公平,更可靠的AI工具提供了一条途径.
- 该方法在临床决策支持中显示出更广泛应用的前景.
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