通过患者标准级公平性约束,实现公平的患者试验匹配
Chia-Yuan Chang1, Jiayi Yuan2, Sirui Ding1
1Texas A&M University, College Station, TX, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
深度学习提高了患者和试验的匹配,但可能会有偏见. 本研究引入了公平匹配框架,以解决公平性问题,并减少对所有患者群体的临床试验招募方面的偏见.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床试验管理 临床试验管理
- 医疗保健中的人工智能
背景情况:
- 临床试验对于新疗法开发至关重要,但在患者招募和保留方面却存在困难.
- 深度学习模型通过评估资格标准相似性来增强患者和试验的匹配.
- 现有的深度学习方法可能会出现公平性问题,特别是对于代表性不足的患者群体.
研究的目的:
- 开发一个公平的患者试验匹配框架,以解决深度学习模型中的偏见.
- 引入患者标准水平的公平性约束,以减轻差异.
- 确保在临床试验招生中提供公平的代表性和准确的数据.
主要方法:
- 提出一个新的公平的患者试验匹配框架.
- 纳入患者标准水平的公平性约束.
- 分析在敏感患者群体中嵌入纳入和排除标准的不一致性.
主要成果:
- 拟议的框架成功地减轻了患者与试验匹配的预测偏差.
- 实验结果表明,在将患者与临床试验相匹配时,公平性得到了改善.
- 该框架有效地处理不同患者群体的资格标准的差异.
结论:
- 公平性约束对于基于深度学习的客观患者试验匹配至关重要.
- 开发的框架提供了一个有希望的解决方案,以提高临床试验招聘的公平性.
- 解决人工智能模型中的偏见对于医学研究的完整性和包容性至关重要.
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